Porphyria s Lover By Robert Browning - 1472 Words

Robert Browning’s dramatic monologue entitled â€Å"Porphyria’s Lover† tells the story of a meeting between a man and a woman that begins filled with romance, but quickly turns sinister. Porphyria visits the speaker at his cottage late at night, to confess her love for him even though they cannot be together. The speaker, filled with happiness in the newfound knowledge that Porphyria â€Å"worshiped† him, kills her by strangling her with her own hair in order to free her from her â€Å"vainer ties† and allow them to be together. He then opens her eyes, props her head on his shoulder, and sits with her all night in an effort to preserve the moment (1278-1279). Perhaps one of his most controversial poems, Browning’s â€Å"Porphyria’s Lover† has been analyzed in various different ways since its publication. Some see it as the simple description of a crime committed by a madman, and others see it as an expression of the male speaker ’s uncontrollable, misogyny fueled desire to possess Porphyria as an object; others still see this poem as a statement on the disadvantageous society where things such as social class and expectations are deciding factors in relationships between men and women. In â€Å"Projection and the Female Other: Romanticism, Browning, and the Victorian Dramatic Monologue,† U. C. Knoepflmacher sees the murder of Porphyria, as well as many of Browning’s other works, as the expression of a man’s desire to possess the â€Å"Female Other,† a concept that in some literary works, women areShow MoreRelatedRobert Browning s Porphyria s Lover 938 Words   |  4 Pagesdeath in different ways. Robert Browning’s poem â€Å"Porphyria’s Lover† has few similarities with â€Å"Do Not Go Gentle into that Good Night† by Dylan Thomas. These authors have drastic differences when talking about death. Browning discusses how killing is a power play in a poem about the speaker gaining control, and Thomas talks about the transience of life in a poem about fighting death. In one of Robert Browning’s most unsettling dramatic monologues, â€Å"Porphyria’s Lover,† Browning tells a story of a manRead MorePorphyria s Lover By Robert Browning936 Words   |  4 PagesRobert Browning’s â€Å"Porphyria’s Lover† is a perfect representation of the status of women during the Victorian Era; women were treated as objects not people. They were property of men, not individuals. In this poem, the speaker, Porphyria’s lover, murders Porphyria and does not only think it was okay to do so, but he also thinks what he has done is noble. In the lines shown above, the speaker begins to realize that Porphyria loves him. Not only does she love him, but she â€Å"worships† him. This furtherRead MoreRobert Browning s Porphyria s Lover967 Words   |  4 Pagesinstance, the poet, Robert Browning relates how an obsessive relationship can change someone’s life in a blink of an eye. Through the use of personification, imagery and character, Browning’s poem â€Å"Porphyria’s Lover† proves that love can make anyone deranged. First, Browning uses personification to demonstrate the idea that love can make someone disturbed, for example, â€Å"The sullen wind was soon awake,† (Line 2). As the character in this poem sits in his dark cottage pinning over his lover, he labels theRead MorePorphyria s Lover By Robert Browning And The Wind1830 Words   |  8 Pages In the poems, â€Å"Porphyria’s Lover† By Robert Browning and â€Å"The Wind† by William Morris, the poets, both share the events surrounding the murder of a young woman, however â€Å"The wind† is widely considered to be set 25 years later and reflection of the murder in â€Å"Porphyria’s Lover†. Both poems are told by an unreliable narrator, who forces the readers to question exactly what occurred. In the poems, the reader can draw similarities and differences between how the unreliable narrator distorts our viewRead MoreAnalysis Of The Poem Porphyria s Lover By Robert Browning1959 Words   |  8 PagesWith so much poetry coming out of Britain it can be hard for any of it to stand out from the rest, but â€Å"Porphyria’s Lover† by Robert Browning and â€Å"A Poison Tree† by William Blake manage to stand out from other poems. These two poems differ in structure, writing style, and voice but both have something that sticks them out from the rest; murderers without a moral compass. While murder isn’t new to poetry it is rare to find it as nonchalant as it is in these two poems. These killers were not killingRead MoreA Brief Note On Robert Browning s Porphyria s Lover And The Laboratory1972 Words   |  8 PagesContributory Factors to the Murders in Robert Browning’s â€Å"Porphyria’s Lover† and â€Å"The Laboratory.† The word ‘love’ possesses such complexity and magnitude that people commonly have a hard time defining it effectively without oversimplifying. Given the true intensity of feeling, jealousy is often said to be synonymous with being in love and the real impact love can have on a person can be unpredictable. Considering this, The Cambridge Dictionary defines a ‘crime of passion’ as a crimeRead MoreAnalysis Of `` Annabel Lee By Edgar Allan Poe1235 Words   |  5 PagesAnnabel Lee by Edgar Allan Poe was inspired by the women that had passed away in his life, but since Poe had written the poem after his wife s death, it is probably more about her. In Porphyria s Lover by Robert Browning, there was no real inspiration except the fact he was just very into dramatic love. Robert Browning in the impulsive Porphyria s Lover and Edgar Allan Poe in the somber Annabel Lee explore the theme of complicated love all throughout their poems and also their use of visualRead MoreThe Power Of Dark Love1217 Words   |  5 Pagesin â€Å"Porphyria’s Lover† says, â€Å"That moment she was mine, mine, fair, perfectly pure and good† (Browning, lines 36-37). Both Robert Browning and Edgar Allan Poe share a love for the themes of obsession, desire, and complicated love. Each of those three themes play an important role in dark love poetry. Each poet describes the main character in their poems as a woman worthy of the speaker’s obsessive, complicated, and desirable love. Both Robert Browning’s tragic â€Å"Porphyria’s Lover† and Edgar AllanRead MorePorphyrias Lover And My Last Duchess By Robert Browning1510 Words   |  7 PagesRobert Browning is a romantic and victorian poet who writes from a speaker’s perspective while a listener is listening to what the speaker reveals about him or herself. Oscar Wilde, author of The Complete Letters of Oscar Wilde, once exclaimed, â€Å"In art, Browning can make action and psychology one!† A healthy and fully expressed relationship is the bond between two people, consisting of trust, honesty, and respect (Denham et al. 397). Within â€Å"Porphyria’s Lover† and â€Å"My Last Duchess,† Robert BrowningRead MoreCompare My Last Duchess And Porphyrias Lover1098 Words   |  5 PagesLove That Kills (Comparing and Contrasting of â€Å"My Last Duchess† and â€Å"Porphyria’s Lover†) While reading the poems â€Å"My Last Duchess† and â€Å"Porphyria’s Lover† by Robert Browning, there is a large possibility that you may be left haunted by the words that were written on the paper. â€Å" This is not to say that he was blandly optimistic, as he is sometimes portrayed. He wrote fully about the world s cruelty and vice and was quite frank that he had himself had no divine revelation. Nevertheless, he resolved

The Dishonesty of Honest People (Paper Summary) - 1589 Words

Dishonesty of Honest People: A theory of Self-Concept maintenance. Main idea ï‚ · ï‚ · ï‚ · People think of themselves as honest. Yes, in reality dishonesty pays quite generously (give examples) The paper demonstrates that their convenience people become dishonest enough to profit but also behave honestly enough to maintain their self-concept. Why are People Dishonest? ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · Origins of theory date from Adam Smith/Thomas Hobbes using Homo Economicus as a base reference. Aka â€Å"Rational Man† who acts consciously and deliberately to trade off benefits and costs of dishonest acts. Within a dishonest act there is normally a balance of tradeoffs: 1. the amount that is to be gained 2. Likelihood†¦show more content†¦Or individuals who came from a society that was organized on Abrahamic religious norms. Would the ten commandments have been effective on Hindus? Or Zoroastrians? Comment [MS4]: Perhaps it had more to do with their knowledge or lack of, in regards to the amount of money available to them as a reward Control group 1(50 cents): No possibility to cheat: as expected Control group 2(2 dollars) No possibility to cheat: as expected Recycle group 1(50 cents):Possibility to cheat: They cheated relative to the ctrl ( but only 13.5% out of 20 max). Cheating was slightly more common in the 50cent condition Recycle group 2 (2 dollars) Possibility to cheat: They cheated relative to those in ctrl (but only 13.5% out of 20 max) Recycle+ Honor code(signed agreement) group1(50 cents): indistinguishable from ctrl (but significantly different from the recycle groups) Recycle+Honor code(signed agreement) group2(2 dollars): indistinguishable from ctrl (but significantly different from recycle groups) ï‚ · Experinment 3: Tested whether a rise categorization malleability, increases level of dishonesty. Whether dishonesty would be motivated by external rewards of money or via intermediary medium(token). Control group 1(50 cents): No possibility to cheat: as expected Comment [MS5]: No support for H1. Easier to claim more in this instance as dishonesty is less salient due to reduced reward level? (They have their ownShow MoreRelatedThe Dishonesty of Honest People (Paper Summary)1597 Words   |  7 PagesDishonesty of Honest People: A theory of Self-Concept maintenance. Main idea ï‚ · ï‚ · ï‚ · People think of themselves as honest. Yes, in reality dishonesty pays quite generously (give examples) The paper demonstrates that their convenience people become dishonest enough to profit but also behave honestly enough to maintain their self-concept. Why are People Dishonest? ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · ï‚ · Origins of theory date from Adam Smith/Thomas Hobbes using Homo Economicus as a base reference. Aka â€Å"RationalRead MoreAcademic Ethics : Academic Integrity1080 Words   |  5 Pagesdefinition, academic integrity stands for the ethical policy or the moral code of the realm of academics. Upholding academic integrity is the responsibility of the students, researchers or academicians. Any person related to academics has to be honest in his or her approach, and that should reflect in the field of research and academic publishing. The person should also be committed to maintain the paramount academic standards in his or her work. Moreover, these should be vehement avoidance ofRead MoreAcademic Misconduct Essay1499 Words   |  6 Pagesthe internet plagiarism has been increasing overtime. We really need to think about the consequences for that. According, to Griffith University (Australia) there are many different kinds of plagiarism. †¢ Presenting a submitting another student’s paper as one’s own. †¢ Paraphrasing an author’s words without proper acknowledgment. †¢ Quoting directly from a source (book, journal, and article) so as to imply that the words are one’s own. Also, plagiarism is a fact of life that is in academic writing;Read MorePlagiarism And Why It Is Considered Wrong1550 Words   |  7 Pagesany research and write a paper. The common assumption on the whole that all of the content and notions of an essay belong to the author, unless the sentence lies within in quotation marks or the summary is cited (Check 21). If the writer utilizes words or concepts from a different source without clear citation of the content, the grader ought to give the writer acclaim for language and notions which in fact belong to a whole different source. The person reading the paper has the responsibility toRead MoreEssay on Judicial Review980 Words   |  4 Pagesindependent judiciary. The Supreme Court of the Netherlands is not a constitutional court and does not have the authority to change an Act of Parliament on the grounds of incompatibility with the Constitution (Constitution, government, , 2003). The people of the Netherlands are looking to change t his government view on judicial review and a proposal is under review presentent by Femeke Halsema in 2002; this proposal has been in review for at least 7 years (Schyff, n.d.). Halsema’s proposal refersRead MoreThe Importance of Ethical Integrity Essay1611 Words   |  7 Pagesimportant? This paper will address the viewpoint of ethical integrity and its outcome. Ethical Integrity is a favorably sensible method for doing what is right when it comes to people dealing with people. In today’s society, ethical integrity has become a modern lifestyle. When a person has a strong moral character, they are said to be a person of integrity and to live a honest life is said to be the most important virtue a person can have. Consistency is a concept of ethical integrity and people should actRead MoreA Project Management Plan For The Orion Shield Project1333 Words   |  6 PagesExecutive Summary A good project management plan takes some preparation it includes the basic concepts of proper planning, organization and great project manager management skills. It includes a variety of resources that come together to achieve a certain goal. As project manager it is imperative that he or she deliver the necessary results within the time limitation as well as within the allotted budget. Effective project managers allocate certain aspects of the project to their team in order toRead MoreThe Increase in Using Technology to Cheat1806 Words   |  8 PagesCheating in the classroom has been happening since the first schoolhouse was built; however, it has more than doubled in the last decade due to the emergence of new technologies that give students high tech alternatives to looking at their classmates paper. A 2002 survey by the Josephson Institute of Ethics of 12,000 high-school students found that 74 % of students had cheated on an exam at least once in the previous year. According to Donald McCabe, who conducted the Rutgers University, New BrunswickRead MoreThe Influence of National Culture on Plagiarism1530 Words   |  6 Pagesstudents. plagiarism justifies academic dishonesty, disrespect, unfairness and irresponsibility because it breaches other peoples intellectual property rights. It devalues the integrity of academic qualifications and discourages students who do not engage in such practices (JISC, 2005). Cultural diversity is assumed to play a huge role in plagiarism with the high level of international students and the presumed difference in educational approach. This paper evaluates the influence of national cultureRead MoreEthics in Academics2371 Words   |  10 PagesCode of Conduct (2008) also mentions the one use of technology that is specifically prohibited in many codes: using the Internet to purchase or copy prepared papers for submission. As stated in the University of Florida Honor Code (2008), the source of these documents â€Å"includes †¦a commercial vendor of research papers, [or] a file of research papers or tests maintained by a student organization or other body or person†. The University of Phoenix Code of Conduct (2008) states: The student must rely

Shafron V Australian Securities and Investments Commission

Question: Discuss about the Shafron V Australian Securities and Investments Commission. Answer: Introduction It was in the year 2012, that a significant clarification of the scope of the duties of the directors of a corporation was provided by the High Court of Australia in the case of Shafron v Australian Securities and Investments Commission (2012) 286 ALR 612 (Shafron) (High Court of Australia, 2012). This was the case in which the extent of the responsibility of an individual who has been granted a position of an officer within the meaning of section 9 of the Corporations Act 2001 (CA). Also, it was clearly stated that an individual who has been an officer could undertake dual roles within an organization under section 9 of the Act as per the extent of responsibility which was provided in this case (Scott, 2012). So, in these kinds of cases the dual roles were not able to be departed for the objectives of examining the duty of care and diligence which was owed by the officer under section 180(1) of the Act. So, in this case, Shafron has specifically clarified what all comprises of participation in making a pronouncement for the objectives of the meaning of the word Officer which was stated in s 9(b) (i) of the CA (Sainty Law, 2012). Background and facts of the Case In August 1998, Mr. Shafron i.e. the plaintiff was appointed by Hardie as a general advocate and company secretary (CS) of the corporation. Though, until November 1998 he was not appointed by the corporation as a CS in a formal manner. In November 1999 with the plaintiff, Mr. Donald Cameron was employed as a united CS of Hardie. In February 2001, the board of directors of Hardie had a meeting in order to consider an offer to depart from the James Hardie group two corporations with major asbestos obligations. The Plaintiff was found to have been in contravention of the section 180(1) of the Act by: Failing to recommend the board of directors of Hardie, that some supplementary data in regard to the partition offer would have been revealed to the Australian Stock Exchange, and Failing to recommend the board of directors of Hardie that an actuarial statement on which the board of directors were dependent in bearing in mind the division proposal did not grant for superimposed price rise when a cautious story would have done so (Tam, 2012). Outline the duties/responsibilities breached and explain why the duties were breached. Section 180(1) of the CA, specifically states that directors of an organization must perform their authorities and fulfill their obligations with the due extent of care and diligence that a prudent individual would work out if they were: The directors or officers of an organization in the situations of an organization; and The officers who were engaged in the office which was held by at the same time had the similar tasks within the organization as, the director or officer (DApice and Curran, 2012). Duties of directors which have been defined under section 180(1) of CA connect not only to directors but to the officers in a broad manner. It was established by the Court of Appeal that the plaintiff was an officer on two different basics as; He was the CS of Hardie, and He was an individual who makes, or contributes in making pronouncements that put an impact upon the entire, or a considerable part, of the trade of the organization. On request, the plaintiff disputed that his duties while being in the position of a CS were restricted to the roles of the CS, and did not broaden to his broad counsel roles (Freeman, 2016). The Plaintiff also want to demarcate his roles while being in the position of a CS by disagreeing that they should be associated to the roles of his cooperative CS, Mr. Cameron, whose roles were chiefly managerial. It was further argued by the plaintiff that he was not an individual who contributed in making the verdict in connection to the parting offer, as it was a pronouncement for the board of directors, of which he was not a part. Lastly, the plaintiff stated that if he was a bureaucrat (on either basis), he had, in any occasion, not violated his obligations in connection both the concern relating to the ASX or the actuarial (Australian Institute of Company Directors, 2017). It was clearly observed by the tribunal that it was significantly to be suspicious for the fact that Mr. Shafron being the director of the corporation could have implemented certain roles in a capacity of a CS and other functions like being a general counsel. There was no proof that the plaintiff has performed some tasks in one capacity and other work in another. Because the responsibilities of a specific CS in specific corporations were the matters of fact, proof of the roles of Mr. Cameron did not show that company secretarial functions of the plaintiff were correspondingly administrative (Hickey and Lam, 2015). The tribunal then measured whether, if the functions of Mr. Shafron could be separated, the division would have an effect on the result. It also renowned that section 180(1) (b) of the Act secures the degree of care and diligence by indicating to the office held and the tasks with the organization of the relevant officer (Comino, 2014). It was then that it was concluded by the tribunal that the term responsibilities which was mentioned in section 180(1) (b) have been defined as the real responsibilities of the authentic officer, not simply the statutory tasks of an individual who holds the office of CS. The findings of the Court of Appeal were at this time was agreed by the tribunals which granted a recommendation in connection to the issue of ASX which was within area of responsibility of the plaintiff. Also, the grant of such suggestion which was granted in connection to the actuarial issue was also within the area of responsibility which was imposed upon the plaintiff being in the position of the director (Wotton Kearney, 2012). Consequently, the responsibilities of Mr. Shafron by being in the position of a company secretary of Hardie were broad enough to hold up a judgment that he owed the duties of an officer in carrying out all of those responsibilities. As per the verdict which was given by the Court of Appeal of New South Wales was appealed and as per the Act the plaintiff was found to be in violation of his obligation of care and diligence by: Failing to give an opinion to the other directors on board that the draft ASX statement which was approved by the Board was deceptive; and Failing to give an opinion to the board that the information which was granted by actuarial Consultants i.e. the plaintiff had been preserved on behalf of JHIL, and This granted the foundation for a cash flow form which was measured by the board in connection to the projected reformation of JHIL, did not take into consideration the the price rises (Mire, 2014). Then the plaintiff approached the High Court and put forward his case. The basic reason of his petition was that though he established that section 180(1) of the CA although would be applicable on him as he was a CS. But the violation of section 180(1) which ASIC had suspected against him were alarmed with measures which he made in his competence as general advocate and not as an executive of JHIL. In other words it could be stated that, his function as general counsel and CS was isolatable into jobs which he embarked on as advocate, and then the responsibilities he assumed as CS. The idea which was provided and mentioned above was rejected that the plaintiff could segregate his tasks and competences. To a certain extent, it was concluded by the tribunal that the responsibilities of Mr. Shafron were inseparable and must be observed as an amalgamated completely (Norton Roseful Bright, 2012). So, in order to reach such conclusion it was specifically established that in order to settle on the range of everyday jobs of an official of a corporation, an individual must inspect all of the work which was carried out for that organization by that officer. In specific it was noted by the tribunal that: The capacity of the role of the plaintiff as a CS could not be determined simply by an evaluation to the function of his co-secretary, whose function never developed to be above merely managerial roles. The Plaintiff did not produced any proof which would have verified or suggested that he carried out certain tasks in the capacity of being a CS, while he carried out others in a different capacity of being a counsellor. The designation of general advocate and CS signified specifically that a significant part of the responsibility of the plaintiff was to take the essential ladders to make sure that JHIL have fulfilled with all pertinent lawmaking necessities. Such requirements include those that were applicable to JHIL as a listed corporation, and that this was related to the stipulation of essential recommendation (Konstantinidis, 2012). When a secured guidance from third parties was taken by the plaintiff then put that guidance before the board of directors of JHIL for its utilization, his liabilities did expanded for recognizing the restrictions of the recommendation which was provided by the third party. So, it was established and pronounced by the tribunal that the extent of care and diligence which was mentioned in section 180(1) of the Act was single-minded by observing at the situations of the organization. The section has also included the workplace and liabilities of a director within the organization that the director in subject had. Therefore, it was clearly specified by the tribunal that to what extend the director would be liable and what responsibilities he had within the organization, in spite of how or why those tasks came to be forced on the official. So, the appeal what the dismissed by the tribunal by stating that a person who was a CS with a lawful background would be predictable to raise issue relating to the probable misleading declarations which were there in revelation duties. Also, it was because of the close participation of the plaintiff in the actuary recommendation that the elevating of the restrictions of that recommendation was an accountability that fell within liability of the plaintiff while being in the position of a CS (Austin, Standen, and Reynolds, 2012). Implications The inference of the verdict which was granted in this case had no doubt been the subject of further discussion and investigation. As what was clearly observed in this case was that a CS who was also general advocate would not be able to simply divide his or her work among the capacity of being a general counsel and CS. It was done more or less, when that conduct was being questioned by the request of section 180(1) of the Act. CS with a lawful backdrop should also take into account that they have to not depend on opinion which was founded from third parties, and should make sure that the board was informed of the restrictions that instruction would have (Jacobson, 2012). Conclusion So, at the end it was concluded that the plaintiff had a far-reaching implications for those helping both as officer, within the meaning of section 9 of the CA and other functions. The verdict makes it clear that the two functions were not departed for the objective of the obligation of care and diligence necessities of the CA. The function of an officer enlarges not only to the legislative responsibilities but to the responsibilities which were essentially undertaken by the officer. So, in this case, the plaintiff could not segregate his responsibilities as the CS and as general counsel. Finally the matter reconfirmed that the standard of care in section 180(1) as integrating the actual responsibilities the officer disturbed had within the corporation not just the statutory responsibilities. And, this verdict served as another advice to those who serve on boards which were covered by the CA, that a high standard of care would be required. Similarly a prejudiced knowledge as well as dedicated responsibilities may enlarge this standard further (Boyce, 2012). References Austin, R., Standen, M., and Reynolds, C. (2012) The High Court decides the James Hardie case. [Online] Minter Ellison. Available from: https://www.minterellison.com/files/uploads/Documents/Publications/Alerts/NA_20120509_JamesHardieDecision.pdf [Accessed on 19/1/17] Australian Institute of Company Directors. (2017) Role of the company secretary.[Online] Australian Institute of Company Directors. Available from: https://aicd.companydirectors.com.au/~/media/cd2/resources/director-resources/director-tools/pdf/05446-6-7-duties-directors_role-company-secretary_a4_web.ashx [Accessed on 19/1/17] Boyce, L. (2012) Shafron v ASIC - general counsel, or counsel of perfection?. [Online] Dibbs Barker. Available from: https://www.dibbsbarker.com/publication/Shafron_v_ASIC_-_general_counsel__or_counsel_of_perfection.aspx [Accessed on 19/1/17] Comino, V. (2014) James Hardie And The Problems Of The Australian Civil Penalties Regime, University of New South Wales Law Journal, 37(1), 195- 207. DApice, B and Curran, C. (2012) Company officers Duty of Care obligations for those who have more than one job description. [Online] Charities Not-For-Profits Law. Available from: https://www.charitiesnfplaw.com.au/2012/07/02/duty-of-care-obligations-for-employees-who-have-more-than-one-job-description/ [Accessed on 19/1/17] Freeman, I. (2016) Shafron V Australian Securities And Investments Commission [2012] Hca 18. [Online] Lavan. Available from: https://www.lavan.com.au/advice/corporate_services/james_hardie_when_is_an_in_house_counsel_liable_as_an_officer_of_a_company [Accessed on 19/1/17] Hickey, M, and Lam, V. (2015) Jumping at shadows shadow and de facto directors. [Online] Sparke Helmore Lawyers. Available from: https://www.sparke.com.au/insights/jumping-at-shadows-shadow-and-de-facto-directors/ [Accessed on 19/1/17] High Court of Australia. (2012) Peter James Shafron V Australian Securities And Investments Commission [2012] HCA 18. [Online] High Court of Australia. Available from: https://www.hcourt.gov.au/assets/publications/judgment-summaries/2012/hcasum18_Shafron_v_ASIC.pdf [Accessed on 19/1/17] Jacobson, D. (2012) ASIC V Shafron: Liability Of Company Secretary (James Hardie). [Online] Bright Law. Available from: https://www.brightlaw.com.au/asic-v-shafron-liability-of-company-secretary-james-hardie/ [Accessed on 19/1/17] Konstantinidis, K. (2012) In-house counsel may be exposed to prosecution, disqualification, penalty or payment of legal costs. [Online] Colin Biggers Paisley Lawyers. Available from: https://www.cbp.com.au/publications/2012/august/in-house-counsel-may-be-exposed-to-prosecution,-di [Accessed on 19/1/17] Mire, S.L. (2014) Its not Fair!: The Duty of Fairness and the Corporate Regulator, Sydney Law Review 36(445), 446. Norton Roseful Bright. (2012) The James Hardie Decisions: Australian Securities Investments Commission v Hellicar Ors [2012] HCA17; Shafron v Australian Securities Investments Commission [2012] HCA 18 [Online] Norton Roseful Bright. Available from: https://www.nortonrosefulbright.com/knowledge/publications/66582/the-james-hardie-decisions-australian-securities-investments-commission-v-hellicar-ors-hca17-shaf [Accessed on 19/1/17] Sainty Law. (2012) Shafron v ASIC: take-aways for General Counsel. [Online] Sainty Law. Available from: https://www.saintylaw.com.au/wp-content/uploads/2012/08/Shafron-Vs-ASIC-takeaway-August-2012.pdf [Accessed on 19/1/17] Scott, P. D. (2012) Shafron v Australian Securities and Investments Commission (2012) 286 ALR 612 , University of Tasmania Law Review 31(2) 155. Tam, K. (2012) The sting for General Counsel in the James Hardie decisions - Shafron v ASIC and ASIC v Hellicar. [Online] Hunt Hunt Lawyers. Available from: https://www.hunthunt.com.au/SiteMedia/w3svc1265/Uploads/Documents/Shafron%20decisionMay2012.pdf [Accessed on 19/1/17] Wotton Kearney. (2012) High Court Rules James Hardie Directors Approved Misleading Asx Release. [Online] Wotton Kearney. Available from: https://www.wottonkearney.com.au/downloads/case%20note%20-%20james%20hardie%20directors%20approved%20for%20misleading%20asx%20release.pdf [Accessed on 19/1/17]

Wuthering Heights Essay Thesis Example For Students

Wuthering Heights Essay Thesis The Substantial Choices that Altered Many DestinationsBy: Rebecca SloanThe Earnshaws and the Lintons both made many substantial choices that arbitrated their egotistic and non-egotistic destinations. Throughout the course of Emily Brontes novel, Wuthering Heights, one may have noted Hareton and Catherines ability to overcome their differences, unlike their parents.Bronte shows the differences between her two main couples through their upbringing, characteristics, and their abilities. The elder Earnshaw and Lintons childhoods are different than the childhoods of their children. The Earnshaws upbringing was done at Wuthering Heights by their father. Wuthering Heights was a dark, stormy place, filled with anger and rejection.Mr. Earnshaw spoils Healthciff and is distraught if anyone shunned him, even if it were his own children. Hindley is the best example; Mr. Earnshaw shipped him away to college so that he could give all of his attention to Healthcliff and Catherine. Although Mr. Earnshaw died Hindley came back and forbid Healthcliff to study. Which automatically degraded Healthcliff to a mere servant on the heights. Through this quote told by Nelly, He drove him from their company to the servants, deprived him of the instructions of the curate, and insisted that he should labor outdoors instead.(38) Hindley pretty much gets total revenge on his father through punishing Healthcliff. Catherine spent five weeks with the Lintons at Thrushcross Grange, a happier home with loving parents and close family bonds.Its inhabitants, Edgar and Isabella, were brought up like royalty, so when Catherine arrived she was spoiled as well, Isabella emptied a plateful of cakes into her lap and.. They dried and combed her beautiful hair, and gave her a pair of enormous slippers, and wheeled her to the fire.(42) This clearly made Catherine more aware of her social status and who she wanted to be. It also opened her eyes to the truth about her true love Healthcliff. If she were to marry a rich man she could save him from her brother Hindley and learn to love Edgar. As she clearly told Nelly from her selfishness in Chapter nine Edgar must shake off his antipathy, and tolerate him I can aid Healthcliff to rise, and place him out of my brothers power This was normal for the time period, however, left Edgar whom truly loved Catherine with the no one to care for him. Edgar was a true man whose only bad trait was, loving Catherine. The children of these characters show stronger will power and the ability to overcome differences. Maybe it was the difference in their childhoods from their parents or that they had characteristics of all to bring them together and dismiss all hatred. Hindleys child Hareton was also brought up at the Heights. He, however, received love from Nelly in his early months. Although, he was very young this could have shifted his whole view of love. Nelly protected him from his fathers fits and loved him like he was her own. Hareton might not have remembered her but deep down knew he was loved. After Nelly left Wuthering Heights, Healthcliff raised Hareton. Due to Heathcliffs revenge on Haretons father Hindley, Hareton was brought up a worker on the farm and was not educated. We will write a custom essay on Wuthering Heights Thesis specifically for you for only $16.38 $13.9/page Order now Catherine and Edgars child, Cathy, was brought up in Edgars home, Thrushcross Grange, in a happy environment. She was loved and sheltered by both her father and Nelly. Because of Edgars faith he was able to move past his loss, Cathys mother Catherine, and focus on loving his daughter. She was however, kept at the Grange and lived a very sheltered life. Her father and Nelly did not tell her about her relatives down the road because of her purity and her well being. Due to her childhood she was enabled to possess many favorable qualities that led her to dominate her own future, unlike her mother. Cathy was able to choose to be happy. She was capable of knowing right and wrong and whom she loves. Cathy knew it was wrong to make fun of Hareton so like her mother, she decided to help educate him. Unlike her mother, she overcame all of her selfishness and realized her love would overcome the unfavorable acts of her uncle. Like Hareton she was robbed of her land and money and forced to roam Wuthering Heights paying off the debts of her forebear. .uc8db2f1599c78dedb176e1e34f7e4955 , .uc8db2f1599c78dedb176e1e34f7e4955 .postImageUrl , .uc8db2f1599c78dedb176e1e34f7e4955 .centered-text-area { min-height: 80px; position: relative; } .uc8db2f1599c78dedb176e1e34f7e4955 , .uc8db2f1599c78dedb176e1e34f7e4955:hover , .uc8db2f1599c78dedb176e1e34f7e4955:visited , .uc8db2f1599c78dedb176e1e34f7e4955:active { border:0!important; } .uc8db2f1599c78dedb176e1e34f7e4955 .clearfix:after { content: ""; display: table; clear: both; } .uc8db2f1599c78dedb176e1e34f7e4955 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .uc8db2f1599c78dedb176e1e34f7e4955:active , .uc8db2f1599c78dedb176e1e34f7e4955:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .uc8db2f1599c78dedb176e1e34f7e4955 .centered-text-area { width: 100%; position: relative ; } .uc8db2f1599c78dedb176e1e34f7e4955 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .uc8db2f1599c78dedb176e1e34f7e4955 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .uc8db2f1599c78dedb176e1e34f7e4955 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .uc8db2f1599c78dedb176e1e34f7e4955:hover .ctaButton { background-color: #34495E!important; } .uc8db2f1599c78dedb176e1e34f7e4955 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .uc8db2f1599c78dedb176e1e34f7e4955 .uc8db2f1599c78dedb176e1e34f7e4955-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .uc8db2f1599c78dedb176e1e34f7e4955:after { content: ""; display: block; clear: both; } READ: Arvin-and-Edgar team bolsters Long Wharf EssayWhen Cathy came to the Heights, Hareton decided, just like Healthciff, that he wanted to learn to show Cathy that he is more than just a laborer. This quote clearly identifies Haretons feelings towards Cathys advent into his life and his evading of Healthcliff. He had been content with daily labor and rough animalenjoyments, til Catherine crossed his path- Shame at her scorn, and hope of her approval were his first prompters to higherpursuits; and instead of guarding him from one, and winning him the other, his endavours to raise himself had produced just thecontrary result. (259) These two pair of lovers made many decisions that altered their futures in many ways. The parents left their children with the same choices to make because of their lack of judgment. Hareton and Cathy were able to overcome many differences between both themselves and their parents. To many peoples surprise their decisions resulted in an almost perfect conclusion, a conclusion and future of unending happiness.

Air Force ROTC Essays - Military Ranks, Free Essays, Term Papers

Air Force ROTC Essays - Military Ranks, Free Essays, Term Papers Air Force ROTC 130th Cadet Wing Cadet Guide Spring 2016 Cadet: Table of Contents Chapter 1: Introduction.... 3 Chapter 2: Contacts, Info Distribution 4 Chapter 3: Customs & Courtesies........... 5 Chapter 4: Physical Fitness Standards & Jodies......... 11 Chapter 5: Drill & Ceremonies.......................... 15 Chapter 6: Uniform Wear.............. 19 Chapter 7: Conclusion 25 Signature Page.26 Chapter 1 Introduction Welcome to the Air Force Reserve Officer Training Corps 130th Cadet Wing! If you are reading this handbook, you have decided to make the initial steps in learning to become a successful Airman and leader in the United States Air Force. The 130th Cadet Wing participates in a leadership laboratory, also known as LLab, which will help you be prepared for Active Duty, and the opportunities it brings. This handbook is the official 130th Cadet Wing, Cadet Guide, which is designed to give you a well-rounded knowledge of Air Force basics, uniform wear, customs & courtesies, and the like. This guide is not a replacement of the requirements mandated by the leadership lab syllabus or the instruction of a cadre member and/or a cadet officer, Air Force Instructions (AFIs), or AFROTC Instructions (AFROTCIs). Rather, it serves as a supplemental material and guide toward where to learn more about your expectations as a cadet in the ROTC program, here, at Cadet Wing 130. Study hard, be confident, never fail, and never leave an Airman behind! Make sure your flight mates and fellow GMC know about this book and learn of its contents. You will be expected to use what you learn in this book at Det 130 and in your Air Force future. If you have any questions about the material, contact your flight mates first, then your POC Flight Commander. Good luck! Chapter 2 Contact Information & References Detachment 130 Contact Information 2419 6th Street, Northwest, Douglass Hall B-29 Washington, DC 20059 Phone: 202-806-6788 Fax: 202-806-4506 Cadet Wing 130 Website: www.det130.org Air Force Website: www.airforce.com Air Force ROTC Website: www.afrotc.com Cadre Members: Lieutenant Colonel Joyner Detachment Commander Captain Richard Frantz Operations Flight Commander Technical Sergeant Anndee Troxler NCOIC, Personnel Staff Sergeant Pamela Torres NCOIC, Knowledge Operations Ms. Ralphine Pughsley Administrative Assistant Information Distribution: Each week the cadet leadership will publish a document called Operations Orders (Ops Orders). This document details what the cadet wing will be doing throughout the week. It lists times, location, and uniform of the day (UOD) for every Leadership Lab as well as physical training session. They are published two ways. First, they will be emailed out over the wing list-serv (to get added to this listserv, email the webmaster at [emailprotected]). The second way is they will be posted to the cadet wing website (see above). The same method of distribution will apply to any other important announcement. If you were to have a question about any ops orders or announcement made you would direct that question through your chain of command (see Chapter 3, Number X). References (source of information in Guide)*: AFI 36-2905: Fitness Program AFI 36-2903: Dress and Appearance of Air Force Personnel AFMAN 36-2203: Drill & Ceremonies Manual Holm Center T-203: AFROTC Field Training Manual Holm Center T-703: Holm Center Training Manual AFROTCI 36-2010: Cadet Training Programs AFROTCI 36-2011: Cadet Operations *These guides can be located online with any internet search engine Chapter 3 Customs, Courtesies and Detachment Etiquette I. Absence and Tardiness Military Tardiness Standard As a cadet, you are expected to be early (usually 15 minutes) for any military-related event. If you are going to be late let your flight commander or supervisor know ahead of time. Be professional; phone calls, e-mails, and text messages are appropriate however, remember to use your customs and courtesies at ALL TIMES! AFROTC Attendance Policy In AFROTC, 80% of your attendance is the minimum standard. As officers, we exceed the minimum. If you need to be excused for academic, work-related, family, or emergency reasons, you need to send an Absence Request Form (ARF) to your direct superior at least 48 hours prior to the scheduled event. In the case of failure to meet attendance minimums, disenrollment from the AFROTC program will be considered. II. Military Etiquette Conduct while in Uniform Never walk on the grass (unless for reveille or related ceremonies). Spitting, chewing tobacco or gum and smoking are not permitted in formation. Cadets should avoid these actions in uniform as they detract from professional appearance. When consuming food or beverage in uniform, do so in a professional manner.

No Country for English

No Country for English No Country for English No Country for English By Maeve Maddox In preparing to write a review of No Country for Old Men, I glanced at some online discussions of the film to see what other people were saying. The grammarian in me overcame the movie critic as I found myself paying more attention to the mode of expression than the thoughts being expressed. In Dustins Review of the film I found three items that distracted me from the content. 1. Of the character Sheriff Ed Tom Bell, the reviewer observes just as he has previously laid witness to similar atrocities over the decades One lays claim to something, but one simply witnesses an atrocity. 2. Again speaking of the sheriff, the reviewer says all he tragically finds in Gods place is an empty void Since the word void means empty or an empty place. it seems a case of belt and suspenders to talk about an empty void. 3. Of the killer, played by Javier Bardem, the reviewer says Whenever he comes in contact with someone, the viewer holds their breath, quite aware of the extent to which he is capable of. In addition to the agreement problem of the viewer holds their breath (which some readers may wish to defend), theres another problem: quite aware of the extent to which he is capable of. Three separate idioms have been crammed into one convoluted sentence. Lets break it down. This killer is a psychopath who kills human beings the way farmers slaughter beef. Very quickly the movie-goer knows that this person would as soon kill you as look at you. The viewer, therefore, is aware of what the killer is capable of. The viewer is aware of the extent of the killers depravity. The viewer is aware of the extremes to which the killer will go. A lot of work has gone into the site on which this review appears. It may contain some outstanding reviews. Its a shame that the first article Ive read contains such careless writing. I now hesitate to look at the others. Want to improve your English in five minutes a day? Get a subscription and start receiving our writing tips and exercises daily! Keep learning! Browse the Grammar category, check our popular posts, or choose a related post below:15 Terms for Those Who Tell the Future3 Cases of Complicated HyphenationPhrasal Verbs and Phrasal Nouns

How male and female Regard Interaction and Leadership Differences in Essay

How male and female Regard Interaction and Leadership Differences in the business communication - Essay Example This is particularly brought about by the perceived inferiority of women, a notion that is largely driven by historical gender inequalities. Despite the extant differences, its should also be noted that they do not form a basis for stereotyping whereby one gender is considered as having better or more advanced communication skills and thus better placed to be effective and competent leaders. On the contrary, in spite of the differences, experience has shown that men and women are still able to meet certain goals and emerge as business leaders with equally effective and good communication skills (Winter, Neal and Waner, 2001). This paper, therefore, explores the differences in how males and females regard interactions and leadership in business setting by looking at the physiological and psychological gender differences, task differences, expertise differences, differences in communication and leadership styles and draws on a conclusion on how they impact on their roles as leaders in at the work place. Gender Differences Men and women are two distinct genders with totally different physiological attributes as evident in the manner in which they act, communicate and methods they employ to influence others around them. These gender differences in communication and influence tactics have a significant role to play in defining their leadership styles at the workplace. Academic research has shown that men have a higher likelihood of being chosen as leaders than men while women generally take considered to take a backstage position and deal with everyday tasks. This is mainly because men and women view the purpose of communication from totally two different perspectives. While men use communication as a way of exerting dominance over others and achieving tangible results, women employ language as a medium of enhancing social connections and creating durable relationships with those around them (Wood, 1996). Another physiological difference between men and women is tha t while the female gender is generally considered to be more expressive, cautious and courteous in their verbal interactions with others, men on the other hand are more assertive, and power hungry (Basow and Rubenfield, 2003). It is these differences in the physiological and psychological nature of the two genders that make men to be likely chosen as leaders as they are more assertive and always desire to posses power, while women, showing tentativeness in their interactions, usually stay in the background and are mainly involved in the everyday tasks. John Gray (1992) identified different communication styles depicted by men and women, suggesting that men are more likely to be goal oriented and are mainly driven by the desire to achieve results. Women on the other hand vale fostering of relations and mainly define desired accomplishments by the type of relationships they build over time. More are more of introverts when it comes to dealing with problem situations as they prefer to keep to themselves while women prefer to talk out issues and involve the opinion of others. Task Differences One difference that stands out between men and w

Industrial Relation Essay Example | Topics and Well Written Essays - 250 words

Industrial Relation - Essay Example Restaurant businesses lack human resource management skills and resources and in turn employees lack union representation A penalty rate is an issue in the industrial relations facing by the restaurant industry (taken to also include cafà © operators and catering providers, but excluding large franchise operators). Penalty rates in particular have apparently caused restaurant owners cost difficulties. The employer pays the penalty for requiring workers to work at unsociable times such as late at night, weekends and public holidays. In the restaurant industry Saturday penalty rates are 1.25 times ordinary earnings, on Sundays the rate is 1.5 times, and on public holidays the rate is 2.5 times. Overtime, that is work beyond ordinary hours, also attracts penalty rates. The union view is that staff should not have to work at minimum wages during unsociable hours. The essential point of conflict for the restaurant industry is the need for some protection of the unskilled and vulnerable workforce contrasted with the need for restaurant owners to achieve an adequate level of business profit and return on equ ity in a very competitive and low margin business. This type of IR framework increases staff hiring pressure, because owners and managers face increased employment risks. The above trends indicate that penalty rates are likely to increase the risk of a restaurants failure. Small restaurant operators are very likely to use family members or ‘safe’ employees extensively to avoid industrial risks. The pluralist approach assumes that any employment relationship automatically has the potential for conflict and this is why effective conflict management so important, and this is the aim of Fair Work Australia. The role of the state is to protect the weak and to try and reconcile conflicting opinions and to keep conflict within

Religion and So-called German Christians Essay Example for Free

Religion and So-called German Christians Essay Religion continues to be one of the most influential forces in the world. It has been seen to provide great peace and harmony to believers, but it has also been the cause if not an actual reason for some of historys greatest wars. It can be used as justification from leaders of war, can it also serve as an instrument of resolution as well? Religion has come to be a much stronger force than any material incentives. It is far better at directing positive behaviour towards social betterment than any laws or physical force. For instance, Ghandi and Martin Luther King Jr conducted non-violent protests based upon religious beliefs. Religion can also be used to help bring people together as they are more willing to work together. By contrast, places that reject religion, such as Revolutionary France, communist Russia and China, or Nazi Germany are often very brutally oppressive. However, it is not always the case. For example, Iran: a country where religion is so prevalent is equally as oppressive as these countries. Incidentally, religion can be very dangerous because it can and has been used to justify horrific acts. Crusaders not only killed many Muslims, they also massacred many Jews and Eastern Christians in the process of attempting to win control of the Holy Land. Adolf Hitlers followers among them the so-called German Christians were believers in their Fuhrer. The Inquisition carried out its torture in the name of God. Religion should never be involved in politics because it can then be used as an instrument of control. However, in places where religion develops freely and people have free access to places of worship, it provides people with a sense of hope, praying serves as therapy and members of a congregation feel a sense of community and friendship. Some of the greatest works of art were created in the name of God. Furthermore, Woodrow Wilson suggested that a strong affinity exists between religious commitment and patriotism. Love of country, just like the love of God certainly inspires good deeds but not always. Furthermore, religion may have led to the creation of the worlds finest art, but it has also caused its destruction. Religion can be a source of extreme nationalism. In Christianity, Islam and Judaism, God is described as mighty warrior, just king and righteous judge. He apparently punished the unjust, the unrighteous and the disobedient. The idea that a nation is the instrument of Gods will has led to war and the subjugation of people viewed as ungodly. Fundamentalism clouds everything. There is a need to be right and superior, which develops an ego and once we fall foul to it, we are lost. Conversely, biblical commandments are the basis of Western ethical and legal systems. It has seen to teach us tolerance for people with other beliefs and opinions. Usually believers are more peaceful, law-abiding and tolerant than the non-believers in the world. Some people need it psychologically and without it may step to extremism such as suicide or anarchy. However, some live in a delusion and in a constant state of intellectual dishonesty. Some people, who are so far devout in their own beliefs, use them to beat other beliefs and religious groups into submission. Religions like Islam justify holy wars against the unfaithful, meaning those of other religions. This can also be seen in the violence of the crusades launched by Christians in the medieval period and by later wars between Protestant and Catholic. Religious conviction like the extremist Hindu groups against Christians and Muslims in India has paved the way for the terrorist attacks in New York City on September eleventh 2001. Religious clashes have led to some of the most heinous human behaviour in recorded history. Western states grew as a result of religion and religious philosophy. Western European and North American societies are still based on Protestant ideals of diligence, thrift and moderation. The very existence of theocratic state, proves that governments in these states are much more stable than regimes in secular countries because leaders are viewed as appointed by God. Political stability, in turn, leads to economic welfare. Despite this, Theocratic states become totalitarian regimes because they are based upon obedience to a ruler who is seen as Gods representative rather than a democratic constitution. They may be stable but they are not essentially concerned with their peoples welfare. By prioritising religious imperatives over economic development and by their intolerance of the questionable types who drive economic process states like Iran have become corrupt, authoritarian and poor. In conclusion, I believe that religion provides many opportunities and hope for those who have nothing else to turn to, it helps bring unity and sometimes peace into communities. However, I also believe that it has become a perversion of the redemptive message of Jesus, by so-called devout humans using their beliefs to ruin and destroy the lives of many others. I myself am agnostic and am very open-minded.

Use of Brucellosis in Bioterrorism Essay -- Biological Terrorism Terro

Bioterrorism Brucellosis is a very threatening biological weapon in the sense that it does not cause fatality, but incapacitates its victims. Not only this, but it is hard to diagnose since the symptoms it induces are extremely nonspecific. Bioterrorism has existed for countless years, and there is no doubt that it will be used in the future. The only thing we have to worry about now is how it will be put to use. Despite going through the trouble of setting up pacts to prevent the use of biological weapons, its presence continues to cause problems on a worldwide scale. It is often said that what we fear the most is in fact fear itself. Happiness cannot negate it, but simply aids in distracting the mind from it; ignorance, on the other hand, harbors fear and provides it a space to grow and envelop the mind. This feeling of terror and insecurity arises from any situation that is presented to us in which we have no control over and is not within the boundaries of our own comfort zones. This flaw in man’s mental state has set in stone a gateway that has led to man’s greatest achievement. It is indeed a terrifying accomplishment, but none would be wise to deny its genius. The theory of this horrifying weapon has been used for thousands of years, and the world may someday fall at its feet. It would be a fallacy to say that it will not be used for years and years to come. Everything aforementioned can be summed up in one word: bioterrorism. Although a plethora of biological agents exist, one in particular acts as one of the most imp ortant destructors regarding animal disease—Brucellosis (Grey). Whenever the word â€Å"bioterrorism† is mentioned, one of the first agents people naturally think of is Bacillus anthracis, the ... ... It is quite a shame that the executive powers in the world feel the need to have such dangerous chemicals and substances to make them feel safe. There is much at stake with countries harboring deadly weapons, and the future seems bleak for generations to come. Works Cited Alton, G. G. and J.R.L. Forsyth. â€Å"Brucella.† Date unknown. 20 July 2007. . Anderson, Burt. Microorganisms and Bioterrorism. New York: Springer, 2006. â€Å"Bacterial Weapon Acting on Humans and Livestock.† Date unknown. 23 July 2007. . â€Å"Division of Bacterial and Mycotic Diseases: Brucellosis.† 6 Oct. 2005. 20 July 2007. . Grey, Michael and Kenneth Spaeth. The Bioterrorism Sourcebook. New York: McGraw-Hill, 2006. Use of Brucellosis in Bioterrorism Essay -- Biological Terrorism Terro Bioterrorism Brucellosis is a very threatening biological weapon in the sense that it does not cause fatality, but incapacitates its victims. Not only this, but it is hard to diagnose since the symptoms it induces are extremely nonspecific. Bioterrorism has existed for countless years, and there is no doubt that it will be used in the future. The only thing we have to worry about now is how it will be put to use. Despite going through the trouble of setting up pacts to prevent the use of biological weapons, its presence continues to cause problems on a worldwide scale. It is often said that what we fear the most is in fact fear itself. Happiness cannot negate it, but simply aids in distracting the mind from it; ignorance, on the other hand, harbors fear and provides it a space to grow and envelop the mind. This feeling of terror and insecurity arises from any situation that is presented to us in which we have no control over and is not within the boundaries of our own comfort zones. This flaw in man’s mental state has set in stone a gateway that has led to man’s greatest achievement. It is indeed a terrifying accomplishment, but none would be wise to deny its genius. The theory of this horrifying weapon has been used for thousands of years, and the world may someday fall at its feet. It would be a fallacy to say that it will not be used for years and years to come. Everything aforementioned can be summed up in one word: bioterrorism. Although a plethora of biological agents exist, one in particular acts as one of the most imp ortant destructors regarding animal disease—Brucellosis (Grey). Whenever the word â€Å"bioterrorism† is mentioned, one of the first agents people naturally think of is Bacillus anthracis, the ... ... It is quite a shame that the executive powers in the world feel the need to have such dangerous chemicals and substances to make them feel safe. There is much at stake with countries harboring deadly weapons, and the future seems bleak for generations to come. Works Cited Alton, G. G. and J.R.L. Forsyth. â€Å"Brucella.† Date unknown. 20 July 2007. . Anderson, Burt. Microorganisms and Bioterrorism. New York: Springer, 2006. â€Å"Bacterial Weapon Acting on Humans and Livestock.† Date unknown. 23 July 2007. . â€Å"Division of Bacterial and Mycotic Diseases: Brucellosis.† 6 Oct. 2005. 20 July 2007. . Grey, Michael and Kenneth Spaeth. The Bioterrorism Sourcebook. New York: McGraw-Hill, 2006.

Zombie Survival Guide

Our world has experienced many pandemics over the course of history, from the bubonic plaque to the HIV/AIDS virus. But what happens when a virus brings us back from the dead. I’m talking about zombies, the undead. Today I will show you how to adequately prepare and survive a zombie apocalypse. Everything from identifying the enemy to defending your home and riding out the storm. Before we can divulge into preparations upon yourself and your home, we must first identify the enemy. What is a Zombie? This West African word defines a Zombie as â€Å"an animated corpse that feeds on living human flesh. Though many Hollywood movies have depicted Zombies as supernatural beings, with extreme strength and cognitive abilities, these accusations are wildly inaccurate. Zombies, in a sense, are basically human. They have the same attributes as humans do. They neither create nor destroy any part of their body (unless physical maiming has occurred before the reanimation). Zombie’s p hysical attributes are basically the same to humans. Their eyes are still capable of transmitting and interpreting visual signals. They seem to have excellent hearing, capable of detecting sound and determine its location. Their smell is more acute, though they are able to distinguish between living prey and the already dead. Since Zombies are the â€Å"Un-Dead†, most of their nerves have also died, leaving the Zombie incapable of touch. This proves to be one advantage the Zombie has over humans. Without the ability to feel pain, Zombies can perform at a higher level of stamina than humans, being able to push themselves further and faster despite the pain it would cause a normal human. Despite Hollywood fiction, Zombies do not possess self-healing abilities, they cut and bleed like normal humans. But as previously stated they do not feel such pain. The Zombie is a ruthless killing machine, it takes wits and brains to conquer such beasts, and a little fire power won’t hurt either. When preparing for the Zombie Apocalypse, choosing the right weapons can be a matter of life and death. Contrary to popular belief, loading up with the heaviest, most powerful weapons ready to kick ass is basically suicidal. Proper consideration should be taken when choosing your weaponry. First and foremost, you should always obey state and federal laws regarding lethal weapons. No matter what weapon you choose, it’s basically useless without proper training. Your weapon should become an extension of your body. If your weapon can be disassembled, you should learn how to take apart your weapon and reassemble it in the dark, it should become second nature. You should care for your weapon as if it were a member of your family, possibly the crazy uncle that could go off at any moment. Be cautious of replica weapons, as these are generally used as movie props and displays. When choosing your weapon, consider your situation. Are you in a close combat situation, or have you fortified your home adequately to where you can shoot from a distance. For blades, the Japanese Katana is ideal. It’s incredibly sharp blade and light weight makes it ideal for slicing through bone and muscle. Remember blades do not need reloading. As for guns, there is a wide range of guns to consider. Everything from heavy machine guns to shotguns. While each have their own pros and cons, for the sake of time we’ll focus on the ones imperative for survival. Heavy machine guns have a high capacity for ammo, though the weight would slow you down on the run, these weapons are best suited for stationary use. The assault rifle is significantly lighter and with the option for single fire or rapid fire. Though the urge to empty a clip into a Zombie body may be tempting, resisting that urge is imperative to save ammo. The semi-automatic rifle has shown itself to be a superior zombie killer. With its high caliber bullets and sharp accuracy, it makes mincemeat of zombies. In a close combat situation, the shotgun reigns supreme. Though its range is limited, due to the pellet pattern, this weapon will surely suffice in close combat. Finally we come to the pistol. This should be reserved as a secondary weapon; it will prove to be your best friend. Now that we have covered who we’re running from and how to defend and kill them, we focus our attention to fortifying and defending our homes. We live in a rural area, and if history teaches us anything; it’s that major outbreaks are mostly concentrated in major cities. Though no house built in the modern era was designed to defend against a zombie attack, modifications should be used to fortify your home. Protecting your home from the undead is virtually the same as defending against human intruders, though ADT alarms will not help you. Secure iron bars should be placed in window sills to prevent zombies from climbing in the windows. In addition to bars, tempered glass should replace pane glass windows to avoid shattering; giving you added time once a horde comes. Any type of fencing should be placed around the perimeter of your property. The higher the better as zombies are not great climbers. Strength in your fencing is everything, as a horde of zombies can easily take down a weak fence. Consider using concrete or cinder blocks and add strength. Proper supplies should be in place to endure the time spent in your home during an outbreak. Water, and lots of it, is the key to survival; about 3 quarts a day per person. Canned foods and dried preserved foods, in addition to portable electric stoves will keep stomachs full. In addition, a gasoline generator and bicycle generator should be in place should the power grid fail. An advanced medical kit, complete with field surgery implements and antibiotics will treat minor and major injuries. Flashlights, lamps and radios should be used and back up batteries should be in place. A high powered telescope, emergency flares and chemical light sticks should finish of your survival equipment. During the attack, you should designate one corner of your back yard for your latrine, also creating a self-sufficient garden for fruits and vegetables, as far away from the latrine as possible. For electrical needs, unless the power grid is still functioning, use the bicycle powered generator. Not only will it keep your home powered, it will keep you physically fit. Patrol your home constantly, on a 24 hour basis. The chance of survival is higher in numbers. Keeping up moral in the group will significantly increase your chance of survival too. Create a library of books and games to keep you entertained. Make sure you have everything in place, ready to be used at a moment’s notice. Everything from an escape route to a survival packs for on the run. Leave no stone unturned and no corner cut when preparing for an attack. Hopefully I have shown you today to properly prepare for a zombie outbreak. And hopefully you heed my warnings. These people are not your neighbors, your school teachers, or even your loving grandmother. These are zombies, the undead. It’s us against them. A battle to the end. Remember to stay alert, stay prepared, and stay alive.

How Does Golding Present Simon in Lord of the Flies-What Is His Role? Essay

William Goldings â€Å"Lord of the flies†, portrays a group of boys who find themselves stranded on a desert island in a deep battle between civilisation and primitive savagery. One of the boys portrayed, Simon, a boy who is kind and physically fragile expresses a deeper knowledge of the problems on the island that the other boys are unaware of. There are many differing viewpoints on his role in the novel. One of these is that he is a biblical parallel; Simon portrays a saintly figure, and shows many of the qualities demonstrated by Jesus Christ. He demonstrates a strong connection with nature throughout, and also is shown to be a character of strong goodwill and kindness. One of the reasons Simon is often thought of as a biblical parallel to Jesus Christ is because of his encounter with â€Å"The beast†, which shows a strong resemblance to Jesus’ 40 days in the desert, in which he encountered the Devil and was tempted by him to leave his mission. In â€Å"Lord of the Flies†, Simon meets the beast during an epileptic fit. His mind, or the Beast tells him â€Å"We are going to have fun on this island†¦so don’t try it on†¦Ã¢â‚¬ . Simon is being told that he must not tell the others what he knows, that they must have fun and Simon must not interfere, but he must just â€Å"run off and play†. The name â€Å"Lord of the Flies† is a translation of a word thought to mean a powerful demon, or the devil himself. This shows that Simon may have represented Jesus in the novel. Simon’s death also shows resemblance to that of Jesus, which shows us that Simon may be Christ’s representation in the novel. ‘Simon was crying out something of a dead man on a hill. ‘ This imagery is displayed just before Simon is mercilessly slaughtered by the other boys, a direct link to the image of Jesus’ crucifixion on the top of a hill. After Christ was killed, it was said that ‘There was darkness over all the land†¦and the earth did quake, and the rocks rent.’ These supernatural biblical descriptions recount the solar eclipse and the earthquake which took place after Jesus’ death. This relates to the death of Simon; ‘The clouds opened and let down the rain like a waterfall’, signifying his death. When Jesus was crucified, he died to save mankind. This relates to Simon confronting the figure on the mountaintop. When he died â€Å"the parachute took the figure forward†¦and bumped it out to sea†. When Simon died, the dead air pilot was finally  released from his purgatorial state on the island, and as Jesus died and salvaged mankind, Simon died and exorcised the soul who lingered between life and death, and between heaven and hell. This shows us that Simon had many strong parallels with Jesus and may have been his biblical parallel in the novel. Simon is a representation of hope and innocence on the island, and has a strong and vital connection with nature. In the novel, Simon has a special place in the forest where he can go and sit alone, at one with his surroundings. â€Å"The sunlight pelted down and the butterflies danced.† This description indicates that this place where Simon often resides is almost paradisiacal, and Simon is able to appreciate the true beauty of the island; it’s beauty is not thwarted by his presence, as it is with the other boys. Simon has a strong connection with the island and with nature itself. Simon is able to see the true beauty in the things that others cannot. The ‘creepers’, which had once hindered the boys and were previously referred to as ‘snake-like’, aid him and form ‘a large mat’. This shows his unity with nature. When Simon ventures into the forest the white ‘candle buds’ open themselves up, which and returned to peaceful place that he came from. Simon is connected with nature throughout the novel, but is only truly at one with it when he dies. The body of the dead airman being pulled out to sea could also symbolise Simons soul being finally released from the confines of life and returning to nature where it belongs. Simon’s true sense of pure goodwill is first demonstrated in his expressed concern for the more vulnerable boys. He help the littluns to get fruit, and ‘pulled off the choicest from up in the foliage, passed them back down to the endless, outstretched hands’. He also helps Piggy to get his glasses back when Jack has knocked them off, showing that he does not discriminate against Piggy because he is different but chooses to help him, even if this may cause him to suffer in the future. These may indicate that Simon, though other characters may be thought of as â€Å"good† and kind, such as Piggy or Ralph, Simon shows no flaws at all. Simon possesses a deep knowledge and understanding about the truth of the island and the beast of which the other boys know not. He also seems to posses many mystic qualities. He is the first to understand truly that the beast is not a physical or material being, but something that lives within the boys. Unlike piggy or Ralph, who are able to appreciate adult knowledge and understanding, Simon possesses  the ability to see the darker side of knowledge. For Simon, the eyes of the Pig’s head on the stick are â€Å"dim with the infinite cynicism of adult life†, meaning that adults believe nothing is ideal, therefore his realisation in itself is cynical– the beast lives within the children, making Simon distrust the human nature. He knows the truth but is unable to get it across to the other boys; â€Å"Simon became inarticulate in his effort to express mankind’s’ essential illness†. Simon understands the truth behind the beast- that the beast itself thrives within the boys, is not something living that can be hunted, but is mankind’s â€Å"essential illness†, the evil that lurks within all men, waiting to be released. The fact that Simon is â€Å"inarticulate† shows that he is unable to express the truth to the others, and even if he had, the boys would not have listened or cared. His death relates to the elements- his knowledge is elemental . Golding shows us the significance of his death by shifting the focus from the movements of Simon’s body to the movements of the sun, the moon and the stars, inciting that Simon’s knowledge was as essential as the elements themselves. However, even though he is portrayed as a weak character right from the beginning, Simon is much braver than he seems; he is the one who climbs the mountain to encounter â€Å"the beast†, at which he discovers that the beast is not material. Simon possesses a number of mystical qualities, such as his pathetic abilities. Simon prophesizes to Ralph â€Å"you’ll get back to where you came from†, almost indicating that he may not. Simon foresees his own death and predicts that Ralph will ret urn home. Another way in which Simon’s mystical qualities are shown is when he asks the other boys rhetorical questions which require much thought to answer, such as â€Å"what is the dirtiest thing there is?† All of these indicate that Simon is a mystic and possesses a much deeper and darker knowledge than the other boys. To conclude, Golding’s presentation of Simon shows us his biblical parallel with Jesus and his significant unity with nature, which lets Simon see the true beauty of the island where others cannot, indicating that it is not the island that is against them but that the boys are against the island, truly centralised around destruction and savagery. He represents Hope and bravery and has a deep and perhaps dark knowledge, which enables him to perceive the truth much more clearly than the other boys. He also has a flawless good will. This shows us that Simon’s true role in the novel was that he alone  had the power to save the boys from themselves, for he alone had the knowledge of the beast’s true nature. Simon was killed because of this, however he was killed as the beast – yet it is ironic that he said the beast was â€Å"only us†. Simon was the least beast-like, which makes one wonder whether him being less savage makes him more or less human.

A Guide to the Barbary Pirates

A Guide to the Barbary Pirates The Barbary pirates (or, more accurately, Barbary privateers) operated out of four North African basesAlgiers, Tunis, Tripoli and various ports in Moroccobetween the 16th and 19th centuries. They terrorized seafaring traders in the Mediterranean Sea and the Atlantic Ocean, sometimes, in the words of John Biddulphs 1907 history of piracy, venturing into the mouth of the [English} channel to make a capture. The privateers worked for North African Muslim deys, or rulers, themselves subjects of the Ottoman Empire, which encouraged privateering as long as the empire received its share of tributes. Privateering had two aims: to enslave captives, who were usually Christian, and to ransom hostages for tribute. The Barbary pirates played a significant role in defining the foreign policy of the United States in its earliest days. The pirates provoked the United States first wars in the Middle East, compelled the United States to build a Navy, and set several precedents, including hostage crises involving the ransoming of American captives and military American military interventions in the Middle East that have been relatively frequent and bloody since. The Barbary wars with the United States ended in 1815 after a naval expedition ordered to North Africas shores by President Madison defeated the Barbary powers and put an end to three decades of American tribute payments. Some 700 Americans had been held hostage over the course of those three decades. Meaning of Barbary The term Barbary was a derogatory, European and American characterization of North African powers. The term is derived from the word barbarians, a reflection of how Western powers, themselves often slave-trading or slave-holding societies at the time, viewed Muslim and Mediterranean regions. Also Known As: Barbary corsairs, Ottoman corsairs, Barbary privateers, Mohammetan pirates

The Role of Chief Justice of the United States

The Role of Chief Justice of the United States Often incorrectly called the chief justice of the Supreme Court, the chief justice of the United States is the nation’s highest-ranking judicial official, and speaking for the judicial branch of the federal government, and serving as the chief administrative officer for the federal courts. In this capacity, the chief justice heads the Judicial Conference of the United States, the chief administrative body of the U.S. federal courts,  and appoints the director of the Administrative Office of the United States Courts. A Chief Justices Main Duties As primary duties, the chief justice presides over oral arguments before the Supreme Court and sets the agenda for the courts meetings. Of course, the chief justice presides over the Supreme Court, which includes eight other members called associate justices. The chief justices vote carries the same weight as those of the associate justices, though the role does require duties that the associate justices dont perform. As such, the chief justice is traditionally paid more than the associate justices. The 2018 annual salary of the chief justice set by Congress, is $267,000, slightly higher than the $255,300 salary of the associate justices. When voting with the majority in a case decided by the Supreme Court, the chief justice may choose to write the Courts opinion  or to assign the task to one of the associate justices. History of the Chief Justice Role The office of chief justice is not explicitly established in the U.S. Constitution. While Article I, Section 3, Clause 6 of the Constitution refers to a chief justice as presiding over Senate trials of presidential impeachment.  Article III, Section 1 of the Constitution, which establishes the Supreme Court itself, refers to all members of the Court simply as â€Å"judges.† The distinct titles of Chief Justice of the Supreme Court of the United States and Associate Justice of the Supreme Court of the United States were created by the Judiciary Act of 1789. In 1866, Associate Justice Salmon P. Chase, who had been by to the Court by President Abraham Lincoln in 1864, convinced Congress to change the official title Chief Justice of the Supreme Court of the United States to the current Chief Justice of the United States. Chase reasoned that the new title better acknowledged the position’s duties within the judicial branch not directly related to the Supreme Court’s deliberations. In 1888, Chief Justice of the United States Melville Fuller became the first person to actually hold the modern title. Since 1789, 15 different presidents have made a total of 22 official nominations to either the original or the modern chief justice position. Since the Constitution mandates only that there must be a chief justice, the practice of appointment by the president with the consent of the Senate has been based solely on tradition. The Constitution does not specifically prohibit the use of other methods, as long as the chief justice is selected from among the other sitting justices. Like all federal judges, the chief justice is nominated by the president of the United States and must be confirmed by the Senate. The term-in-office of the chief justice is set by Article III, Section 1 of the Constitution, which states that all federal judges shall hold their offices during good behavior, meaning that chief justices serve for life, unless they die, resign, or are removed from office through the impeachment process. Presiding Over Impeachments and Inaugurations The chief justice sits as the judge in  impeachments  of the president of the United States,  including when the vice  president of the United States  is the acting president. Chief Justice Salmon P. Chase presided over the Senate trial of President  Andrew Johnson  in 1868, and Chief Justice  William H. Rehnquist  presided over the trial of President William Clinton in 1999. While its thought the chief justice must swear in ​presidents at inaugurations, this is a purely traditional role. According to law, any federal or state judge is empowered to administer oaths of office, and even a notary public can perform the duty, as was the case when Calvin Coolidge was sworn in as president in 1923. Procedure and Reporting and Inaugurations In day-to-day proceedings, the chief justice enters the courtroom first and casts the first vote when the justices deliberate, and also presides over closed-door conferences of the court in which votes are cast on pending appeals and cases heard in oral argument. Outside the courtroom, the chief justice writes an annual report to Congress about the state of the federal court system and appoints other federal judges to serve on various administrative and judicial panels. The chief justice also serves as chancellor of the Smithsonian Institution  and sits on the boards of the National Gallery of Art and the Hirshhorn Museum.

Current event analysis Essay Example | Topics and Well Written Essays - 250 words

Current event analysis - Essay Example ates that the state of Florida announced they are facing a $3 billion dollar deficit and as a result the mental health programs could be cut by as much as thirty to fifty percent by the Florida House and the Florida Senate. Jacque Henderson, director of Tri-County Human Services residential programs in Lakeland spoke about the potential cuts and said that almost every significant advance in the last thirty-years, including drug court, mental health court, and miscellaneous treatment services, are all at risk of being cut. The current House plan is to cut over $172 million, while the Senate has a proposal for $205 million in cuts. Expressing similar sentiments to Jacque Henderson, Partners in Crisis was also highly concerned about the potential of losing drug court and mental health court programs. The article states that the Peace River staff has already taken 5% pay cuts, and only through a large private donation were they able to remain a viable institution. While the article does mention research that states recent statistical analysis states that the programs that might be cut have demonstrated significant preventative value, it doesn’t elucidate on these findings. While mental health programs should be of the utmost concern and valued as highly as other medical programs, one would hope to see some fiscal responsibility and proof of

Learning Theory and Simulation Applications for Aviation Training Essay

Learning Theory and Simulation Applications for Aviation Training - Essay Example The use of simulation training in the preparation of military and civilian pilots cannot be ignored and this is mainly used because training based on actual equipment in the real world can be prohibitively expensive and dangerous. "In fact, the military and the commercial aviation industry are probably the biggest investors in simulation-based training. These simulations range in cost, fidelity, and functionality. Many simulation systems have the ability to mimic detailed terrain, equipment failures, motion, vibration, and visual cues about a situation." (Salas and Cannon-Bowers, 2001, p 471). In the training of military and civilian pilots, three essential learning theories such as behaviorism, cognitivism and constructivism are employed. One of the major concepts which guide the design of flight training simulators today is that "transfer of training is highest when similarity of the training and transfer situations is the highest . . . this is the governing principle for most simu lators that are built." ( Adams, 1979). Therefore, simulation training can be comprehended as one of the most effective and practical methods of aviation training which corresponds to the utility of learning theories in the training of civil and military training. Simulation training reflects the most advantageous outcomes

Qatar Essay Example | Topics and Well Written Essays - 1500 words - 1

Qatar - Essay Example Qatar is a little peninsula that is on the western shore of the Arabian Gulf and it covers approximately 4,247 square miles (6,286 square kilometers). Qatar is just 160 kilometers north into the Persian Gulf from Saudi Arabia. It is located between latitudes 24Â ° and 27Â ° N, and longitudes 50Â ° and 52Â ° E. Qatar mostly consist of low and barren plain that is covered with sand. To the southeast lies the Khor al Adaid (‘Inland Sea’), which is a region of rolling sand dunes surrounding Persian Gulf’s inlet? The landmass creates a rectangle that is described by the local folklore as resembling right hand’s palm that is extended in a prayer. The neighboring nations include Iran to the northeast. Bahrain to the northwest, Saudi Arabia and the United Arab Emirates to the south. Both Qatar and Bahrain claim the Hawar Islands located west of Qatar and it is uninhabited. Just recently, only few semi-permanent seasonal encampments have been found in the interi or desert. Resources of water that are near the coast together with opportunities for pearl diving, fishing, and seagoing trade have facilitated larger, and additional permanent settlements. The patterns of these settlements have contributed to the social differences between Hadar and Bedouin. Qatar’s climate can be described as subtropical dry, hot desert climate that has low annual rainfall. During the summer the temperatures are extremely high and there is a big difference between maximum and minimum temperatures, more so in the inland areas. The Persian Gulf slightly influences the coastal areas and have lower maximum, however, it has higher minimum temperatures and the moisture percentage in the air are higher. Summer ‘June – September’ is extremely hot with low rainfall. Daily maximum temperatures are able to easily reach 40Â °C or more. Winter is cooler with irregular rainfall.

Identifying Clusters in High Dimensional Data

Identifying Clusters in High Dimensional Data â€Å"Ask those who remember, are mindful if you do not know).† (Holy Quran, 6:43) Removal Of Redundant Dimensions To Find Clusters In N-Dimensional Data Using Subspace Clustering Abstract The data mining has emerged as a powerful tool to extract knowledge from huge databases. Researchers have introduced several machine learning algorithms to explore the databases to discover information, hidden patterns, and rules from the data which were not known at the data recording time. Due to the remarkable developments in the storage capacities, processing and powerful algorithmic tools, practitioners are developing new and improved algorithms and techniques in several areas of data mining to discover the rules and relationship among the attributes in simple and complex higher dimensional databases. Furthermore data mining has its implementation in large variety of areas ranging from banking to marketing, engineering to bioinformatics and from investment to risk analysis and fraud detection. Practitioners are analyzing and implementing the techniques of artificial neural networks for classification and regression problems because of accuracy, efficiency. The aim of his short r esearch project is to develop a way of identifying the clusters in high dimensional data as well as redundant dimensions which can create a noise in identifying the clusters in high dimensional data. Techniques used in this project utilizes the strength of the projections of the data points along the dimensions to identify the intensity of projection along each dimension in order to find cluster and redundant dimension in high dimensional data. 1 Introduction In numerous scientific settings, engineering processes, and business applications ranging from experimental sensor data and process control data to telecommunication traffic observation and financial transaction monitoring, huge amounts of high-dimensional measurement data are produced and stored. Whereas sensor equipments as well as big storage devices are getting cheaper day by day, data analysis tools and techniques wrap behind. Clustering methods are common solutions to unsupervised learning problems where neither any expert knowledge nor some helpful annotation for the data is available. In general, clustering groups the data objects in a way that similar objects get together in clusters whereas objects from different clusters are of high dissimilarity. However it is observed that clustering disclose almost no structure even it is known there must be groups of similar objects. In many cases, the reason is that the cluster structure is stimulated by some subsets of the spaces dim ensions only, and the many additional dimensions contribute nothing other than making noise in the data that hinder the discovery of the clusters within that data. As a solution to this problem, clustering algorithms are applied to the relevant subspaces only. Immediately, the new question is how to determine the relevant subspaces among the dimensions of the full space. Being faced with the power set of the set of dimensions a brute force trial of all subsets is infeasible due to their exponential number with respect to the original dimensionality. In high dimensional data, as dimensions are increasing, the visualization and representation of the data becomes more difficult and sometimes increase in the dimensions can create a bottleneck. More dimensions mean more visualization or representation problems in the data. As the dimensions are increased, the data within those dimensions seems dispersing towards the corners / dimensions. Subspace clustering solves this problem by identifying both problems in parallel. It solves the problem of relevant subspaces which can be marked as redundant in high dimensional data. It also solves the problem of finding the cluster structures within that dataset which become apparent in these subspaces. Subspace clustering is an extension to the traditional clustering which automatically finds the clusters present in the subspace of high dimensional data space that allows better clustering the data points than the original space and it works even when the curse of dimensionality occurs. The most o f the clustering algorithms have been designed to discover clusters in full dimensional space so they are not effective in identifying the clusters that exists within subspace of the original data space. The most of the clustering algorithms produces clustering results based on the order in which the input records were processed [2]. Subspace clustering can identify the different cluster within subspaces which exists in the huge amount of sales data and through it we can find which of the different attributes are related. This can be useful in promoting the sales and in planning the inventory levels of different products. It can be used for finding the subspace clusters in spatial databases and some useful decisions can be taken based on the subspace clusters identified [2]. The technique used here for indentifying the redundant dimensions which are creating noise in the data in order to identifying the clusters consist of drawing or plotting the data points in all dimensions. At second step the projection of all data points along each dimension are plotted. At the third step the unions of projections along each dimension are plotted using all possible combinations among all no. of dimensions and finally the union of all projection along all dimensions and analyzed, it will show the contribution of each dimension in indentifying the cluster which will be represented by the weight of projection. If any of the given dimension is contributing very less in order to building the weight of projection, that dimension can be considered as redundant, which means this dimension is not so important to identify the clusters in given data. The details of this strategy will be covered in later chapters. 2 Data Mining 2.1 What is Data Mining? Data mining is the process of analyzing data from different perspective and summarizing it for getting useful information. The information can be used for many useful purposes like increasing revenue, cuts costs etc. The data mining process also finds the hidden knowledge and relationship within the data which was not known while data recording. Describing the data is the first step in data mining, followed by summarizing its attributes (like standard deviation mean etc). After that data is reviewed using visual tools like charts and graphs and then meaningful relations are determined. In the data mining process, the steps of collecting, exploring and selecting the right data are critically important. User can analyze data from different dimensions categorize and summarize it. Data mining finds the correlation or patterns amongst the fields in large databases. Data mining has a great potential to help companies to focus on their important information in their data warehouse. It can predict the future trends and behaviors and allows the business to make more proactive and knowledge driven decisions. It can answer the business questions that were traditionally much time consuming to resolve. It scours databases for hidden patterns for finding predictive information that experts may miss it might lies beyond their expectations. Data mining is normally used to transform the data into information or knowledge. It is commonly used in wide range of profiting practices such as marketing, fraud detection and scientific discovery. Many companies already collect and refine their data. Data mining techniques can be implemented on existing platforms for enhance the value of information resources. Data mining tools can analyze massive databases to deliver answers to the questions. Some other terms contains similar meaning from data mining such as â€Å"Knowledge mining† or â€Å"Knowledge Extraction† or â€Å"Pattern Analysis†. Data mining can also be treated as a Knowledge Discovery from Data (KDD). Some people simply mean the data mining as an essential step in Knowledge discovery from a large data. The process of knowledge discovery from data contains following steps. * Data cleaning (removing the noise and inconsistent data) * Data Integration (combining multiple data sources) * Data selection (retrieving the data relevant to analysis task from database) * Data Transformation (transforming the data into appropriate forms for mining by performing summary or aggregation operations) * Data mining (applying the intelligent methods in order to extract data patterns) * Pattern evaluation (identifying the truly interesting patterns representing knowledge based on some measures) * Knowledge representation (representing knowledge techniques that are used to present the mined knowledge to the user) 2.2 Data Data can be any type of facts, or text, or image or number which can be processed by computer. Todays organizations are accumulating large and growing amounts of data in different formats and in different databases. It can include operational or transactional data which includes costs, sales, inventory, payroll and accounting. It can also include nonoperational data such as industry sales and forecast data. It can also include the meta data which is, data about the data itself, such as logical database design and data dictionary definitions. 2.3 Information The information can be retrieved from the data via patterns, associations or relationship may exist in the data. For example the retail point of sale transaction data can be analyzed to yield information about the products which are being sold and when. 2.4 Knowledge Knowledge can be retrieved from information via historical patterns and the future trends. For example the analysis on retail supermarket sales data in promotional efforts point of view can provide the knowledge buying behavior of customer. Hence items which are at most risk for promotional efforts can be determined by manufacturer easily. 2.5 Data warehouse The advancement in data capture, processing power, data transmission and storage technologies are enabling the industry to integrate their various databases into data warehouse. The process of centralizing and retrieving the data is called data warehousing. Data warehousing is new term but concept is a bit old. Data warehouse is storage of massive amount of data in electronic form. Data warehousing is used to represent an ideal way of maintaining a central repository for all organizational data. Purpose of data warehouse is to maximize the user access and analysis. The data from different data sources are extracted, transformed and then loaded into data warehouse. Users / clients can generate different types of reports and can do business analysis by accessing the data warehouse. Data mining is primarily used today by companies with a strong consumer focus retail, financial, communication, and marketing organizations. It allows these organizations to evaluate associations between certain internal external factors. The product positioning, price or staff skills can be example of internal factors. The external factor examples can be economic indicators, customer demographics and competition. It also allows them to calculate the impact on sales, corporate profits and customer satisfaction. Furthermore it allows them to summarize the information to look detailed transactional data. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by its capabilities. Data mining usually automates the procedure of searching predictive information in huge databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data very quickly. The targeted marketing can be an example of predictive problem. Data mining utilizes data on previous promotional mailings in order to recognize the targets most probably to increase return on investment as maximum as possible in future mailings. Tools used in data mining traverses through huge databases and discover previously unseen patterns in single step. Analysis on retail sales data to recognize apparently unrelated products which are usually purchased together can be an example of it. The more pattern discovery problems can include identifying fraudulent credit card transactions and identifying irregular data that could symbolize data entry input errors. When data mining tools are used on parallel processing systems of high performance, they are able to analy ze huge databases in very less amount of time. Faster or quick processing means that users can automatically experience with more details to recognize the complex data. High speed and quick response makes it actually possible for users to examine huge amounts of data. Huge databases, in turn, give improved and better predictions. 2.6 Descriptive and Predictive Data Mining Descriptive data mining aims to find patterns in the data that provide some information about what the data contains. It describes patterns in existing data, and is generally used to create meaningful subgroups such as demographic clusters. For example descriptions are in the form of Summaries and visualization, Clustering and Link Analysis. Predictive Data Mining is used to forecast explicit values, based on patterns determined from known results. For example, in the database having records of clients who have already answered to a specific offer, a model can be made that predicts which prospects are most probable to answer to the same offer. It is usually applied to recognize data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest. For example, a credit card company may want to engage in predictive data mining, to derive a (trained) model or set of models that can quickly identify tr ansactions which have a high probability of being fraudulent. Other types of data mining projects may be more exploratory in nature (e.g. to determine the cluster or divisions of customers), in which case drill-down descriptive and tentative methods need to be applied. Predictive data mining is goad oriented. It can be decomposed into following major tasks. * Data Preparation * Data Reduction * Data Modeling and Prediction * Case and Solution Analysis 2.7 Text Mining The Text Mining is sometimes also called Text Data Mining which is more or less equal to Text Analytics. Text mining is the process of extracting/deriving high quality information from the text. High quality information is typically derived from deriving the patterns and trends through means such as statistical pattern learning. It usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. The High Quality in text mining usually refers to some combination of relevance, novelty, and interestingness. The text categorization, concept/entity extraction, text clustering, sentiment analysis, production of rough taxonomies, entity relation modeling, document summarization can be included as text mining tasks. Text Mining is also known as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Linking together of the extracted information is the key element to create new facts or new hypotheses to be examined further by more conventional ways of experimentation. In text mining, the goal is to discover unknown information, something that no one yet knows and so could not have yet written down. The difference between ordinary data mining and text mining is that, in text mining the patterns are retrieved from natural language text instead of from structured databases of facts. Databases are designed and developed for programs to execute automatically; text is written for people to read. Most of the researchers think that it will need a full fledge simulation of how the brain works before that programs that read the way people do could be written. 2.8 Web Mining Web Mining is the technique which is used to extract and discover the information from web documents and services automatically. The interest of various research communities, tremendous growth of information resources on Web and recent interest in e-commerce has made this area of research very huge. Web mining can be usually decomposed into subtasks. * Resource finding: fetching intended web documents. * Information selection and pre-processing: selecting and preprocessing specific information from fetched web resources automatically. * Generalization: automatically discovers general patterns at individual and across multiple website * Analysis: validation and explanation of mined patterns. Web Mining can be mainly categorized into three areas of interest based on which part of Web needs to be mined: Web Content Mining, Web Structure Mining and Web Usage Mining. Web Contents Mining describes the discovery of useful information from the web contents, data and documents [10]. In past the internet consisted of only different types of services and data resources. But today most of the data is available over the internet; even digital libraries are also available on Web. The web contents consist of several types of data including text, image, audio, video, metadata as well as hyperlinks. Most of the companies are trying to transform their business and services into electronic form and putting it on Web. As a result, the databases of the companies which were previously residing on legacy systems are now accessible over the Web. Thus the employees, business partners and even end clients are able to access the companys databases over the Web. Users are accessing the application s over the web via their web interfaces due to which the most of the companies are trying to transform their business over the web, because internet is capable of making connection to any other computer anywhere in the world [11]. Some of the web contents are hidden and hence cannot be indexed. The dynamically generated data from the results of queries residing in the database or private data can fall in this area. Unstructured data such as free text or semi structured data such as HTML and fully structured data such as data in the tables or database generated web pages can be considered in this category. However unstructured text is mostly found in the web contents. The work on Web content mining is mostly done from 2 point of views, one is IR and other is DB point of view. â€Å"From IR view, web content mining assists and improves the information finding or filtering to the user. From DB view web content mining models the data on the web and integrates them so that the more soph isticated queries other than keywords could be performed. [10]. In Web Structure Mining, we are more concerned with the structure of hyperlinks within the web itself which can be called as inter document structure [10]. It is closely related to the web usage mining [14]. Pattern detection and graphs mining are essentially related to the web structure mining. Link analysis technique can be used to determine the patterns in the graph. The search engines like Google usually uses the web structure mining. For example, the links are mined and one can then determine the web pages that point to a particular web page. When a string is searched, a webpage having most number of links pointed to it may become first in the list. Thats why web pages are listed based on rank which is calculated by the rank of web pages pointed to it [14]. Based on web structural data, web structure mining can be divided into two categories. The first kind of web structure mining interacts with extracting patterns from the hyperlinks in the web. A hyperlink is a structural comp onent that links or connects the web page to a different web page or different location. The other kind of the web structure mining interacts with the document structure, which is using the tree-like structure to analyze and describe the HTML or XML tags within the web pages. With continuous growth of e-commerce, web services and web applications, the volume of clickstream and user data collected by web based organizations in their daily operations has increased. The organizations can analyze such data to determine the life time value of clients, design cross marketing strategies etc. [13]. The Web usage mining interacts with data generated by users clickstream. â€Å"The web usage data includes web server access logs, proxy server logs, browser logs, user profile, registration data, user sessions, transactions, cookies, user queries, bookmark data, mouse clicks and scrolls and any other data as a result of interaction† [10]. So the web usage mining is the most important task of the web mining [12]. Weblog databases can provide rich information about the web dynamics. In web usage mining, web log records are mined to discover the user access patterns through which the potential customers can be identified, quality of internet services can be enhanc ed and web server performance can be improved. Many techniques can be developed for implementation of web usage mining but it is important to know that success of such applications depends upon what and how much valid and reliable knowledge can be discovered the log data. Most often, the web logs are cleaned, condensed and transformed before extraction of any useful and significant information from weblog. Web mining can be performed on web log records to find associations patterns, sequential patterns and trend of web accessing. The overall Web usage mining process can be divided into three inter-dependent stages: data collection and pre-processing, pattern discovery, and pattern analysis [13]. In the data collection preprocessing stage, the raw data is collected, cleaned and transformed into a set of user transactions which represents the activities of each user during visits to the web site. In the pattern discovery stage, statistical, database, and machine learning operations a re performed to retrieve hidden patterns representing the typical behavior of users, as well as summary of statistics on Web resources, sessions, and users. 3 Classification 3.1 What is Classification? As the quantity and the variety increases in the available data, it needs some robust, efficient and versatile data categorization technique for exploration [16]. Classification is a method of categorizing class labels to patterns. It is actually a data mining methodology used to predict group membership for data instances. For example, one may want to use classification to guess whether the weather on a specific day would be â€Å"sunny†, â€Å"cloudy† or â€Å"rainy†. The data mining techniques which are used to differentiate similar kind of data objects / points from other are called clustering. It actually uses attribute values found in the data of one class to distinguish it from other types or classes. The data classification majorly concerns with the treatment of the large datasets. In classification we build a model by analyzing the existing data, describing the characteristics of various classes of data. We can use this model to predict the class/type of new data. Classification is a supervised machine learning procedure in which individual items are placed in a group based on quantitative information on one or more characteristics in the items. Decision Trees and Bayesian Networks are the examples of classification methods. One type of classification is Clustering. This is process of finding the similar data objects / points within the given dataset. This similarity can be in the meaning of distance measures or on any other parameter, depending upon the need and the given data. Classification is an ancient term as well as a modern one since classification of animals, plants and other physical objects is still valid today. Classification is a way of thinking about things rather than a study of things itself so it draws its theory and application from complete range of human experiences and thoughts [18]. From a bigger picture, classification can include medical patients based on disease, a set of images containing red rose from an image database, a set of documents describing â€Å"classification† from a document/text database, equipment malfunction based on cause and loan applicants based on their likelihood of payment etc. For example in later case, the problem is to predict a new applicants loans eligibility given old data about customers. There are many techniques which are used for data categorization / classification. The most common are Decision tree classifier and Bayesian classifiers. 3.2 Types of Classification There are two types of classification. One is supervised classification and other is unsupervised classification. Supervised learning is a machine learning technique for discovering a function from training data. The training data contains the pairs of input objects, and their desired outputs. The output of the function can be a continuous value which can be called regression, or can predict a class label of the input object which can be called as classification. The task of the supervised learner is to predict the value of the function for any valid input object after having seen a number of training examples (i.e. pairs of input and target output). To achieve this goal, the learner needs to simplify from the presented data to hidden situations in a meaningful way. The unsupervised learning is a class of problems in machine learning in which it is needed to seek to determine how the data are organized. It is distinguished from supervised learning in that the learner is given only unknown examples. Unsupervised learning is nearly related to the problem of density estimation in statistics. However unsupervised learning also covers many other techniques that are used to summarize and explain key features of the data. One form of unsupervised learning is clustering which will be covered in next chapter. Blind source partition based on Independent Component Analysis is another example. Neural network models, adaptive resonance theory and the self organizing maps are most commonly used unsupervised learning algorithms. There are many techniques for the implementation of supervised classification. We will be discussing two of them which are most commonly used which are Decision Trees classifiers and Naà ¯ve Bayesian Classifiers. 3.2.1 Decision Trees Classifier There are many alternatives to represent classifiers. The decision tree is probably the most widely used approach for this purpose. It is one of the most widely used supervised learning methods used for data exploration. It is easy to use and can be represented in if-then-else statements/rules and can work well in noisy data as well [16]. Tree like graph or decisions models and their possible consequences including resource costs, chance event, outcomes, and utilities are used in decision trees. Decision trees are most commonly used in specifically in decision analysis, operations research, to help in identifying a strategy most probably to reach a target. In machine learning and data mining, a decision trees are used as predictive model; means a planning from observations calculations about an item to the conclusions about its target value. More descriptive names for such tree models are classification tree or regression tree. In these tree structures, leaves are representing class ifications and branches are representing conjunctions of features those lead to classifications. The machine learning technique for inducing a decision tree from data is called decision tree learning, or decision trees. Decision trees are simple but powerful form of multiple variable analyses [15]. Classification is done by tree like structures that have different test criteria for a variable at each of the nodes. New leaves are generated based on the results of the tests at the nodes. Decision Tree is a supervised learning system in which classification rules are constructed from the decision tree. Decision trees are produced by algorithms which identify various ways splitting data set into branch like segment. Decision tree try to find out a strong relationship between input and target values within the dataset [15]. In tasks classification, decision trees normally visualize that what steps should be taken to reach on classification. Every decision tree starts with a parent node called root node which is considered to be the parent of every other node. Each node in the tree calculates an attribute in the data and decides which path it should follow. Typically the decision test is comparison of a value against some constant. Classification with the help of decision tree is done by traversing from the root node up to a leaf node. Decision trees are able to represent and classify the diverse types of data. The simplest form of data is numerical data which is most familiar too. Organizing nominal data is also required many times in many situations. Nominal quantities are normally represented via discrete set of symbols. For example weather condition can be described in either nominal fashion or numeric. Quantification can be done about temperature by saying that it is eleven degrees Celsius or fifty two degrees Fahrenheit. The cool, mild, cold, warm or hot terminologies can also be sued. The former is a type of numeric data while and the latter is an example of nominal data. More precisely, the example of cool, mild, cold, warm and hot is a special type of nominal data, expressed as ordinal data. Ordinal data usually has an implicit assumption of ordered relationships among the values. In the weather example, purely nominal description like rainy, overcast and sunny can also be added. These values have no relationships or distance measures among each other. Decision Trees are those types of trees where each node is a question, each branch is an answer to a question, and each leaf is a result. Here is an example of Decision tree. Roughly, the idea is based upon the number of stock items; we have to make different decisions. If we dont have much, you buy at any cost. If you have a lot of items then you only buy if it is inexpensive. Now if stock items are less than 10 then buy all if unit price is less than 10 otherwise buy only 10 items. Now if we have 10 to 40 items in the stock then check unit price. If unit price is less than 5 £ then buy only 5 items otherwise no need to buy anything expensive since stock is good already. Now if we have more than 40 items in the stock, then buy 5 if and only if price is less than 2 £ otherwise no need to buy too expensive items. So in this way decision trees help us to make a decision at each level. Here is another example of decision tree, representing the risk factor associated with the rash driving. The root node at the top of the tree structure is showing the feature that is split first for highest discrimination. The internal nodes are showing decision rules on one or more attributes while leaf nodes are class labels. A person having age less than 20 has very high risk while a person having age greater than 30 has a very low risk. A middle category; a person having age greater than 20 but less than 30 depend upon another attribute which is car type. If car type is of sports then there is again high risk involved while if family car is used then there is low risk involved. In the field of sciences engineering and in the applied areas including business intelligence and data mining, many useful features are being introduced as the result of evolution of decision trees. * With the help of transformation in decision trees, the volume of data can be reduced into more compact form that preserves the major characteristic Identifying Clusters in High Dimensional Data Identifying Clusters in High Dimensional Data â€Å"Ask those who remember, are mindful if you do not know).† (Holy Quran, 6:43) Removal Of Redundant Dimensions To Find Clusters In N-Dimensional Data Using Subspace Clustering Abstract The data mining has emerged as a powerful tool to extract knowledge from huge databases. Researchers have introduced several machine learning algorithms to explore the databases to discover information, hidden patterns, and rules from the data which were not known at the data recording time. Due to the remarkable developments in the storage capacities, processing and powerful algorithmic tools, practitioners are developing new and improved algorithms and techniques in several areas of data mining to discover the rules and relationship among the attributes in simple and complex higher dimensional databases. Furthermore data mining has its implementation in large variety of areas ranging from banking to marketing, engineering to bioinformatics and from investment to risk analysis and fraud detection. Practitioners are analyzing and implementing the techniques of artificial neural networks for classification and regression problems because of accuracy, efficiency. The aim of his short r esearch project is to develop a way of identifying the clusters in high dimensional data as well as redundant dimensions which can create a noise in identifying the clusters in high dimensional data. Techniques used in this project utilizes the strength of the projections of the data points along the dimensions to identify the intensity of projection along each dimension in order to find cluster and redundant dimension in high dimensional data. 1 Introduction In numerous scientific settings, engineering processes, and business applications ranging from experimental sensor data and process control data to telecommunication traffic observation and financial transaction monitoring, huge amounts of high-dimensional measurement data are produced and stored. Whereas sensor equipments as well as big storage devices are getting cheaper day by day, data analysis tools and techniques wrap behind. Clustering methods are common solutions to unsupervised learning problems where neither any expert knowledge nor some helpful annotation for the data is available. In general, clustering groups the data objects in a way that similar objects get together in clusters whereas objects from different clusters are of high dissimilarity. However it is observed that clustering disclose almost no structure even it is known there must be groups of similar objects. In many cases, the reason is that the cluster structure is stimulated by some subsets of the spaces dim ensions only, and the many additional dimensions contribute nothing other than making noise in the data that hinder the discovery of the clusters within that data. As a solution to this problem, clustering algorithms are applied to the relevant subspaces only. Immediately, the new question is how to determine the relevant subspaces among the dimensions of the full space. Being faced with the power set of the set of dimensions a brute force trial of all subsets is infeasible due to their exponential number with respect to the original dimensionality. In high dimensional data, as dimensions are increasing, the visualization and representation of the data becomes more difficult and sometimes increase in the dimensions can create a bottleneck. More dimensions mean more visualization or representation problems in the data. As the dimensions are increased, the data within those dimensions seems dispersing towards the corners / dimensions. Subspace clustering solves this problem by identifying both problems in parallel. It solves the problem of relevant subspaces which can be marked as redundant in high dimensional data. It also solves the problem of finding the cluster structures within that dataset which become apparent in these subspaces. Subspace clustering is an extension to the traditional clustering which automatically finds the clusters present in the subspace of high dimensional data space that allows better clustering the data points than the original space and it works even when the curse of dimensionality occurs. The most o f the clustering algorithms have been designed to discover clusters in full dimensional space so they are not effective in identifying the clusters that exists within subspace of the original data space. The most of the clustering algorithms produces clustering results based on the order in which the input records were processed [2]. Subspace clustering can identify the different cluster within subspaces which exists in the huge amount of sales data and through it we can find which of the different attributes are related. This can be useful in promoting the sales and in planning the inventory levels of different products. It can be used for finding the subspace clusters in spatial databases and some useful decisions can be taken based on the subspace clusters identified [2]. The technique used here for indentifying the redundant dimensions which are creating noise in the data in order to identifying the clusters consist of drawing or plotting the data points in all dimensions. At second step the projection of all data points along each dimension are plotted. At the third step the unions of projections along each dimension are plotted using all possible combinations among all no. of dimensions and finally the union of all projection along all dimensions and analyzed, it will show the contribution of each dimension in indentifying the cluster which will be represented by the weight of projection. If any of the given dimension is contributing very less in order to building the weight of projection, that dimension can be considered as redundant, which means this dimension is not so important to identify the clusters in given data. The details of this strategy will be covered in later chapters. 2 Data Mining 2.1 What is Data Mining? Data mining is the process of analyzing data from different perspective and summarizing it for getting useful information. The information can be used for many useful purposes like increasing revenue, cuts costs etc. The data mining process also finds the hidden knowledge and relationship within the data which was not known while data recording. Describing the data is the first step in data mining, followed by summarizing its attributes (like standard deviation mean etc). After that data is reviewed using visual tools like charts and graphs and then meaningful relations are determined. In the data mining process, the steps of collecting, exploring and selecting the right data are critically important. User can analyze data from different dimensions categorize and summarize it. Data mining finds the correlation or patterns amongst the fields in large databases. Data mining has a great potential to help companies to focus on their important information in their data warehouse. It can predict the future trends and behaviors and allows the business to make more proactive and knowledge driven decisions. It can answer the business questions that were traditionally much time consuming to resolve. It scours databases for hidden patterns for finding predictive information that experts may miss it might lies beyond their expectations. Data mining is normally used to transform the data into information or knowledge. It is commonly used in wide range of profiting practices such as marketing, fraud detection and scientific discovery. Many companies already collect and refine their data. Data mining techniques can be implemented on existing platforms for enhance the value of information resources. Data mining tools can analyze massive databases to deliver answers to the questions. Some other terms contains similar meaning from data mining such as â€Å"Knowledge mining† or â€Å"Knowledge Extraction† or â€Å"Pattern Analysis†. Data mining can also be treated as a Knowledge Discovery from Data (KDD). Some people simply mean the data mining as an essential step in Knowledge discovery from a large data. The process of knowledge discovery from data contains following steps. * Data cleaning (removing the noise and inconsistent data) * Data Integration (combining multiple data sources) * Data selection (retrieving the data relevant to analysis task from database) * Data Transformation (transforming the data into appropriate forms for mining by performing summary or aggregation operations) * Data mining (applying the intelligent methods in order to extract data patterns) * Pattern evaluation (identifying the truly interesting patterns representing knowledge based on some measures) * Knowledge representation (representing knowledge techniques that are used to present the mined knowledge to the user) 2.2 Data Data can be any type of facts, or text, or image or number which can be processed by computer. Todays organizations are accumulating large and growing amounts of data in different formats and in different databases. It can include operational or transactional data which includes costs, sales, inventory, payroll and accounting. It can also include nonoperational data such as industry sales and forecast data. It can also include the meta data which is, data about the data itself, such as logical database design and data dictionary definitions. 2.3 Information The information can be retrieved from the data via patterns, associations or relationship may exist in the data. For example the retail point of sale transaction data can be analyzed to yield information about the products which are being sold and when. 2.4 Knowledge Knowledge can be retrieved from information via historical patterns and the future trends. For example the analysis on retail supermarket sales data in promotional efforts point of view can provide the knowledge buying behavior of customer. Hence items which are at most risk for promotional efforts can be determined by manufacturer easily. 2.5 Data warehouse The advancement in data capture, processing power, data transmission and storage technologies are enabling the industry to integrate their various databases into data warehouse. The process of centralizing and retrieving the data is called data warehousing. Data warehousing is new term but concept is a bit old. Data warehouse is storage of massive amount of data in electronic form. Data warehousing is used to represent an ideal way of maintaining a central repository for all organizational data. Purpose of data warehouse is to maximize the user access and analysis. The data from different data sources are extracted, transformed and then loaded into data warehouse. Users / clients can generate different types of reports and can do business analysis by accessing the data warehouse. Data mining is primarily used today by companies with a strong consumer focus retail, financial, communication, and marketing organizations. It allows these organizations to evaluate associations between certain internal external factors. The product positioning, price or staff skills can be example of internal factors. The external factor examples can be economic indicators, customer demographics and competition. It also allows them to calculate the impact on sales, corporate profits and customer satisfaction. Furthermore it allows them to summarize the information to look detailed transactional data. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by its capabilities. Data mining usually automates the procedure of searching predictive information in huge databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data very quickly. The targeted marketing can be an example of predictive problem. Data mining utilizes data on previous promotional mailings in order to recognize the targets most probably to increase return on investment as maximum as possible in future mailings. Tools used in data mining traverses through huge databases and discover previously unseen patterns in single step. Analysis on retail sales data to recognize apparently unrelated products which are usually purchased together can be an example of it. The more pattern discovery problems can include identifying fraudulent credit card transactions and identifying irregular data that could symbolize data entry input errors. When data mining tools are used on parallel processing systems of high performance, they are able to analy ze huge databases in very less amount of time. Faster or quick processing means that users can automatically experience with more details to recognize the complex data. High speed and quick response makes it actually possible for users to examine huge amounts of data. Huge databases, in turn, give improved and better predictions. 2.6 Descriptive and Predictive Data Mining Descriptive data mining aims to find patterns in the data that provide some information about what the data contains. It describes patterns in existing data, and is generally used to create meaningful subgroups such as demographic clusters. For example descriptions are in the form of Summaries and visualization, Clustering and Link Analysis. Predictive Data Mining is used to forecast explicit values, based on patterns determined from known results. For example, in the database having records of clients who have already answered to a specific offer, a model can be made that predicts which prospects are most probable to answer to the same offer. It is usually applied to recognize data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest. For example, a credit card company may want to engage in predictive data mining, to derive a (trained) model or set of models that can quickly identify tr ansactions which have a high probability of being fraudulent. Other types of data mining projects may be more exploratory in nature (e.g. to determine the cluster or divisions of customers), in which case drill-down descriptive and tentative methods need to be applied. Predictive data mining is goad oriented. It can be decomposed into following major tasks. * Data Preparation * Data Reduction * Data Modeling and Prediction * Case and Solution Analysis 2.7 Text Mining The Text Mining is sometimes also called Text Data Mining which is more or less equal to Text Analytics. Text mining is the process of extracting/deriving high quality information from the text. High quality information is typically derived from deriving the patterns and trends through means such as statistical pattern learning. It usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. The High Quality in text mining usually refers to some combination of relevance, novelty, and interestingness. The text categorization, concept/entity extraction, text clustering, sentiment analysis, production of rough taxonomies, entity relation modeling, document summarization can be included as text mining tasks. Text Mining is also known as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Linking together of the extracted information is the key element to create new facts or new hypotheses to be examined further by more conventional ways of experimentation. In text mining, the goal is to discover unknown information, something that no one yet knows and so could not have yet written down. The difference between ordinary data mining and text mining is that, in text mining the patterns are retrieved from natural language text instead of from structured databases of facts. Databases are designed and developed for programs to execute automatically; text is written for people to read. Most of the researchers think that it will need a full fledge simulation of how the brain works before that programs that read the way people do could be written. 2.8 Web Mining Web Mining is the technique which is used to extract and discover the information from web documents and services automatically. The interest of various research communities, tremendous growth of information resources on Web and recent interest in e-commerce has made this area of research very huge. Web mining can be usually decomposed into subtasks. * Resource finding: fetching intended web documents. * Information selection and pre-processing: selecting and preprocessing specific information from fetched web resources automatically. * Generalization: automatically discovers general patterns at individual and across multiple website * Analysis: validation and explanation of mined patterns. Web Mining can be mainly categorized into three areas of interest based on which part of Web needs to be mined: Web Content Mining, Web Structure Mining and Web Usage Mining. Web Contents Mining describes the discovery of useful information from the web contents, data and documents [10]. In past the internet consisted of only different types of services and data resources. But today most of the data is available over the internet; even digital libraries are also available on Web. The web contents consist of several types of data including text, image, audio, video, metadata as well as hyperlinks. Most of the companies are trying to transform their business and services into electronic form and putting it on Web. As a result, the databases of the companies which were previously residing on legacy systems are now accessible over the Web. Thus the employees, business partners and even end clients are able to access the companys databases over the Web. Users are accessing the application s over the web via their web interfaces due to which the most of the companies are trying to transform their business over the web, because internet is capable of making connection to any other computer anywhere in the world [11]. Some of the web contents are hidden and hence cannot be indexed. The dynamically generated data from the results of queries residing in the database or private data can fall in this area. Unstructured data such as free text or semi structured data such as HTML and fully structured data such as data in the tables or database generated web pages can be considered in this category. However unstructured text is mostly found in the web contents. The work on Web content mining is mostly done from 2 point of views, one is IR and other is DB point of view. â€Å"From IR view, web content mining assists and improves the information finding or filtering to the user. From DB view web content mining models the data on the web and integrates them so that the more soph isticated queries other than keywords could be performed. [10]. In Web Structure Mining, we are more concerned with the structure of hyperlinks within the web itself which can be called as inter document structure [10]. It is closely related to the web usage mining [14]. Pattern detection and graphs mining are essentially related to the web structure mining. Link analysis technique can be used to determine the patterns in the graph. The search engines like Google usually uses the web structure mining. For example, the links are mined and one can then determine the web pages that point to a particular web page. When a string is searched, a webpage having most number of links pointed to it may become first in the list. Thats why web pages are listed based on rank which is calculated by the rank of web pages pointed to it [14]. Based on web structural data, web structure mining can be divided into two categories. The first kind of web structure mining interacts with extracting patterns from the hyperlinks in the web. A hyperlink is a structural comp onent that links or connects the web page to a different web page or different location. The other kind of the web structure mining interacts with the document structure, which is using the tree-like structure to analyze and describe the HTML or XML tags within the web pages. With continuous growth of e-commerce, web services and web applications, the volume of clickstream and user data collected by web based organizations in their daily operations has increased. The organizations can analyze such data to determine the life time value of clients, design cross marketing strategies etc. [13]. The Web usage mining interacts with data generated by users clickstream. â€Å"The web usage data includes web server access logs, proxy server logs, browser logs, user profile, registration data, user sessions, transactions, cookies, user queries, bookmark data, mouse clicks and scrolls and any other data as a result of interaction† [10]. So the web usage mining is the most important task of the web mining [12]. Weblog databases can provide rich information about the web dynamics. In web usage mining, web log records are mined to discover the user access patterns through which the potential customers can be identified, quality of internet services can be enhanc ed and web server performance can be improved. Many techniques can be developed for implementation of web usage mining but it is important to know that success of such applications depends upon what and how much valid and reliable knowledge can be discovered the log data. Most often, the web logs are cleaned, condensed and transformed before extraction of any useful and significant information from weblog. Web mining can be performed on web log records to find associations patterns, sequential patterns and trend of web accessing. The overall Web usage mining process can be divided into three inter-dependent stages: data collection and pre-processing, pattern discovery, and pattern analysis [13]. In the data collection preprocessing stage, the raw data is collected, cleaned and transformed into a set of user transactions which represents the activities of each user during visits to the web site. In the pattern discovery stage, statistical, database, and machine learning operations a re performed to retrieve hidden patterns representing the typical behavior of users, as well as summary of statistics on Web resources, sessions, and users. 3 Classification 3.1 What is Classification? As the quantity and the variety increases in the available data, it needs some robust, efficient and versatile data categorization technique for exploration [16]. Classification is a method of categorizing class labels to patterns. It is actually a data mining methodology used to predict group membership for data instances. For example, one may want to use classification to guess whether the weather on a specific day would be â€Å"sunny†, â€Å"cloudy† or â€Å"rainy†. The data mining techniques which are used to differentiate similar kind of data objects / points from other are called clustering. It actually uses attribute values found in the data of one class to distinguish it from other types or classes. The data classification majorly concerns with the treatment of the large datasets. In classification we build a model by analyzing the existing data, describing the characteristics of various classes of data. We can use this model to predict the class/type of new data. Classification is a supervised machine learning procedure in which individual items are placed in a group based on quantitative information on one or more characteristics in the items. Decision Trees and Bayesian Networks are the examples of classification methods. One type of classification is Clustering. This is process of finding the similar data objects / points within the given dataset. This similarity can be in the meaning of distance measures or on any other parameter, depending upon the need and the given data. Classification is an ancient term as well as a modern one since classification of animals, plants and other physical objects is still valid today. Classification is a way of thinking about things rather than a study of things itself so it draws its theory and application from complete range of human experiences and thoughts [18]. From a bigger picture, classification can include medical patients based on disease, a set of images containing red rose from an image database, a set of documents describing â€Å"classification† from a document/text database, equipment malfunction based on cause and loan applicants based on their likelihood of payment etc. For example in later case, the problem is to predict a new applicants loans eligibility given old data about customers. There are many techniques which are used for data categorization / classification. The most common are Decision tree classifier and Bayesian classifiers. 3.2 Types of Classification There are two types of classification. One is supervised classification and other is unsupervised classification. Supervised learning is a machine learning technique for discovering a function from training data. The training data contains the pairs of input objects, and their desired outputs. The output of the function can be a continuous value which can be called regression, or can predict a class label of the input object which can be called as classification. The task of the supervised learner is to predict the value of the function for any valid input object after having seen a number of training examples (i.e. pairs of input and target output). To achieve this goal, the learner needs to simplify from the presented data to hidden situations in a meaningful way. The unsupervised learning is a class of problems in machine learning in which it is needed to seek to determine how the data are organized. It is distinguished from supervised learning in that the learner is given only unknown examples. Unsupervised learning is nearly related to the problem of density estimation in statistics. However unsupervised learning also covers many other techniques that are used to summarize and explain key features of the data. One form of unsupervised learning is clustering which will be covered in next chapter. Blind source partition based on Independent Component Analysis is another example. Neural network models, adaptive resonance theory and the self organizing maps are most commonly used unsupervised learning algorithms. There are many techniques for the implementation of supervised classification. We will be discussing two of them which are most commonly used which are Decision Trees classifiers and Naà ¯ve Bayesian Classifiers. 3.2.1 Decision Trees Classifier There are many alternatives to represent classifiers. The decision tree is probably the most widely used approach for this purpose. It is one of the most widely used supervised learning methods used for data exploration. It is easy to use and can be represented in if-then-else statements/rules and can work well in noisy data as well [16]. Tree like graph or decisions models and their possible consequences including resource costs, chance event, outcomes, and utilities are used in decision trees. Decision trees are most commonly used in specifically in decision analysis, operations research, to help in identifying a strategy most probably to reach a target. In machine learning and data mining, a decision trees are used as predictive model; means a planning from observations calculations about an item to the conclusions about its target value. More descriptive names for such tree models are classification tree or regression tree. In these tree structures, leaves are representing class ifications and branches are representing conjunctions of features those lead to classifications. The machine learning technique for inducing a decision tree from data is called decision tree learning, or decision trees. Decision trees are simple but powerful form of multiple variable analyses [15]. Classification is done by tree like structures that have different test criteria for a variable at each of the nodes. New leaves are generated based on the results of the tests at the nodes. Decision Tree is a supervised learning system in which classification rules are constructed from the decision tree. Decision trees are produced by algorithms which identify various ways splitting data set into branch like segment. Decision tree try to find out a strong relationship between input and target values within the dataset [15]. In tasks classification, decision trees normally visualize that what steps should be taken to reach on classification. Every decision tree starts with a parent node called root node which is considered to be the parent of every other node. Each node in the tree calculates an attribute in the data and decides which path it should follow. Typically the decision test is comparison of a value against some constant. Classification with the help of decision tree is done by traversing from the root node up to a leaf node. Decision trees are able to represent and classify the diverse types of data. The simplest form of data is numerical data which is most familiar too. Organizing nominal data is also required many times in many situations. Nominal quantities are normally represented via discrete set of symbols. For example weather condition can be described in either nominal fashion or numeric. Quantification can be done about temperature by saying that it is eleven degrees Celsius or fifty two degrees Fahrenheit. The cool, mild, cold, warm or hot terminologies can also be sued. The former is a type of numeric data while and the latter is an example of nominal data. More precisely, the example of cool, mild, cold, warm and hot is a special type of nominal data, expressed as ordinal data. Ordinal data usually has an implicit assumption of ordered relationships among the values. In the weather example, purely nominal description like rainy, overcast and sunny can also be added. These values have no relationships or distance measures among each other. Decision Trees are those types of trees where each node is a question, each branch is an answer to a question, and each leaf is a result. Here is an example of Decision tree. Roughly, the idea is based upon the number of stock items; we have to make different decisions. If we dont have much, you buy at any cost. If you have a lot of items then you only buy if it is inexpensive. Now if stock items are less than 10 then buy all if unit price is less than 10 otherwise buy only 10 items. Now if we have 10 to 40 items in the stock then check unit price. If unit price is less than 5 £ then buy only 5 items otherwise no need to buy anything expensive since stock is good already. Now if we have more than 40 items in the stock, then buy 5 if and only if price is less than 2 £ otherwise no need to buy too expensive items. So in this way decision trees help us to make a decision at each level. Here is another example of decision tree, representing the risk factor associated with the rash driving. The root node at the top of the tree structure is showing the feature that is split first for highest discrimination. The internal nodes are showing decision rules on one or more attributes while leaf nodes are class labels. A person having age less than 20 has very high risk while a person having age greater than 30 has a very low risk. A middle category; a person having age greater than 20 but less than 30 depend upon another attribute which is car type. If car type is of sports then there is again high risk involved while if family car is used then there is low risk involved. In the field of sciences engineering and in the applied areas including business intelligence and data mining, many useful features are being introduced as the result of evolution of decision trees. * With the help of transformation in decision trees, the volume of data can be reduced into more compact form that preserves the major characteristic