We All Have Experienced an Embarrassing Moment in Our Life

Some of the embarrassing moments in our lives are worth recalling while others are so disgusting that no one would wish to recall or even be associated with them. That day will forever remain in my mind. I can recall with nostalgia an incident, which took place five years ago. Back at home, my parents would reward my brother and me for the good grades we scored in the term papers. On this one occasion, my mum rewarded me with two hundred dollars for the good grades in the end year papers. In order have fun; I decided to invite my best friends to a hotel for lunch.On arrival at the hotel, I told my friends to order as much as they wished to take. One after another, they placed their orders and immediately the waiters swung into action and started serving us. The food was very delicious just as the services. We all took to eating, talking and laughing at the jokes we made back at school. On finishing the food, the bill was placed on the table. I hurriedly reached my pocket to get the m oney but to my utter disbelief, the two hundred dollars were missing. After a thorough check in all my pockets, it dawned on me that I had left my dollars in my bedroom.I had been filled with joy and excitement when my mother gave me that I did not remember to put it in my purse. On waking up in the morning, I was late and so I prepared myself in a hurry leaving the money on the table inside my room. I thought of borrowing money from my comrades but none of them could offer any help. They did not carry any money, as we would be picked by the school bus from home in the morning and only come back in the evening with the same. I approached the waiter and narrated my ordeal but she was fuming with rage.She ordered me to explain to the director. The hotel director was a kind and understanding man. He listened to my rather unfortunate ordeal and allowed me to rush home and get the money but had to leave my school identity card with him just in case I did not to come back. I hurriedly wen t to the nearest telephone booth, called my younger brother, and requested him to deliver the money at the hotel. Ever since that day, I ceased to going to that hotel. I have this fear that some of the waiters might recall my countenance.It was the most embarrassing moment of my life. Since the events of that day, my friends have always made jokes regarding that fateful day and would all laugh at it. The other day when I was going to the cinema I met with an old good friend of mine who was there when all that happened while on summer vacation. We reminisced for quite sometime and he offered to buy me a drink. We hurriedly entered the hotel nearby only to find other two childhood friends. It was a fantastic but strange twist of events as we further talked about the good old times.Food and drinks were served and we started eating, as we talked oblivious of who had served us. When we had spent some good time inside there, we rise up to go when the bill was placed on the table. The wait er was anxiously waiting for the money. My friend got the money and it was only while he was paying that we realized it was in this same hotel and the same waiter who had held us at ransom. She also was recalling but this time she managed to smile at us. We looked at each other and all we could do was to laugh it was just fabulous. It is amazing how sometimes life can be.

“A Separate Peace” by John Knowles Essay

One of the major themes in A Separate Peace is the coming of age. The theme of maturity can be viewed as a growing realization of the war in the school (in which the students realize that they have to enlist into the war â€Å"as men†), or the private and interior crisis one goes through (such as Gene discovering his identity as the novel progresses). The training and the sudden labors that the Devon students engage in attempt to prepare the boys for their future at the war; this can be seen as the external view of maturity in the novel, whereas the â€Å"internal† view of maturity can be seen in Gene’s thoughts as he searches for his personal identity. Throughout the novel, both Gene and Finny experience important yet damaging issues in their life where they realize the need to face the reality of it or become lost forever. As Gene discovers in the end, true identity can only be reached through maturity. Gene and the students of Devon experience a sense of maturity through the sudden change in their once peaceful and war-shunning environment of the summer. In the beginning of the novel, we can see that Devon is like a â€Å"Garden of Eden†; it resembles a paradise in the center of all the wars and deaths that are happening outside Devon’s barriers. Devon is seen as a milieu within a larger milieu (the rest of America at war). It seems that the students have lived their summer in a peaceful bubble of â€Å"Eden† in contrast with the background of World War II in the rest of the world. The summer of 1942 at Devon can be symbolized as the time of freedom and the exposure of youth; this is a moment in the novel where the students can get away with breaking rules and skipping classes. Therefore, the carefree summer of 1942 represents a time of paradise, where everyone is at peace and simply enjoying life at its fullest. However, Finny’s symbolic â€Å"fall† seems to have brought an end to this delight at Devon and brings in the winter session, where there is labour, orders, discipline, darkness, and despair. This is the moment when the teachers of Devon realize that the students are just on their way of serving the army. The students begin to participate in drills and trips to the railroad and orchard to help out in every way they can. In contrast to the summer of Devon, the winter represents the burdens of maturity and adulthood, and a  time where preparation of the war replaces the joyful atmosphere that was present in the summer. The boys of Devon suddenly feel that they must be responsible and â€Å"established† in order to face whatever their future brings them in the war. They all realize that they must smarten up and become men, because it is time to face the reality of what is going on behind Devon’s peaceful barriers. The phrase â€Å"Innocence must be killed to give birth to experience† says a lot about this time in the novel. Though the teachers had given the students more freedom during the summer and allowed several rule-breakings to take place, they understand that in order for the students to be ready and prepared for the coming conflicts in the army they must stop acting like children and sacrifice their state of immaturity to gain knowledge as adults. They understand that children cannot survive in wars, but men can. Later, the students realize that they must enlist themselves to serve for the army within a short period of time. Most of them become excited about becoming a solider for army, but then the novel takes an ironic twist with the students’ beliefs of the war because they do not yet know the real dangers and certainties of the outside of the barriers of Devon (regarding the world war). An example of such â€Å"blind thinking† was Leper becoming the first to join the war, thinking that he will gain more time in the forest afterwards, but returns devastated and emotionally shattered. His confrontation with Gene proves that there is a war out there and it is horrible as well. Gene, after realizing that he may also suffer from the same mental state as Leper if he enlisted, runs away screaming, â€Å"Shut up; it has nothing to do with me so shut up!†. Here we can see that Gene realizes some truth about the war, and no matter how much he tries to deny the horrible details and evidences that Leper brings back from the outside of Devon he gains new insight and wisdom. It is in this sense as well that Gene matures through the pressures of the war in the background, and that he cannot run from it because it is reality and he has to face it when it is his turn to enlist. The presence of the war, in a sense, also serves as a background for the  emotional development of the students at Devon; the world war actually triggers the buried emotions of the boys. Gene, Finny, and Brinker (for example) become competitive in their own ways; Gene compares his academic standards with Finny’s natural talents for sports, Finny shows a â€Å"win-win† competitive nature towards Gene in the games that they have played together (though he is out of the â€Å"war† mentally)), and Brinker feels insecure about his popularity due to Finny. Each character feels unconfident and is therefore â€Å"at war† with himself. In this novel, the ability to fix these inner conflicts seems to sadly result in either death (like Finny), or insanity (like Leper). For Finny, since he is unable to face certain feelings, he ends up becoming upset at the mock trial and dies in the second accident. Leper, on the other hand, believed that by enlisting first would b ring him out from his loneliness, yet returns from the war in a far worse shape. Gene, however, goes through a more painful process by remaining in Devon to fight for salvation within himself. Because the view of maturity in an emotional development is mostly seen in Gene (as narrator, we could see his thought-process as the novel progresses), I will use his private conflicts as an example to further support my thesis. In the beginning of the book, Gene develops a close relationship with Finny, his roommate. However, Gene begins to feel a bit envious of Finny, and sees his way of thinking as the truth. This then lead to an inner conflict in Gene, in which he begins to compare himself with Finny in a â€Å"Win-Lose† way of thinking. As his thinking of â€Å"competition† continues, Gene begins to see certain â€Å"flaws† within himself that leads to his insecurity, though these â€Å"missing traits† are not really flaws. He does tend to â€Å"hold himself back† several times by repeatedly telling himself how lucky he is to have Finny as a best friend, but this excuse soon shatters because he remains selfish. This selfishness of him reveals itself in chapter three, where Finny practically saves Gene from falling, but Gene tries to protect his beliefs of â€Å"Finny being the enemy† by telling himself that it was Finny’s fault for getting him into the me ss in the first place. At the same time, Gene’s admiration for Finny’s personality prevents him from refusing to go out with  Finny; it is in this state that Gene is actually a confused young man, who does not know the true value of friendship, and cannot correct the jealousy that he feels for Finny. The jealousy continues to grow, and soon enough Gene jounces the limb in chapter five, resulting in Finny’s fall. As I have said before, Gene is then forced to review what he has done to Finny and take a good look at himself; his mind, feeling extremely guilty for his actions, pressures him so much about the accident that he is forced to grow up. We see the final stage of maturity in Gene when he realizes near the end of the novel that he needs to become a grown-up and confront his personal war face-to-face once and for all; he confesses to Finny about his part in the accident, and finally gains Finny’s forgiveness and a sense of salvation. It was in this confession that Gene is f orced to see his stupidity and selfishness behind some of his actions. His act of courage to go to Finny and confess is evidence that he has finally grown. The conflict that he feels inside (regarding his relationship with Finny) becomes the source of his final emotional development; because of the â€Å"accident† the he had committed against Finny’s fall, Gene is forced to examine his own feelings over and over again throughout the novel. This repeated painful examination of his feelings and guilt results in growth; by really looking in himself, Gene realizes that he has to be responsible for his actions. It is when Gene finally reaches his peak of maturity that he begins to see his true identity in the end of the novel. Gene has emerged from a sort of shyness into a more confident attitude; he was influenced by Finny to learn about people, events, and life in a way that he had never before. In short, Gene needed Finny in order to realize himself. And sadly, Finny’s death leads to the eulogy that Gene makes in the last chapter, where he remembers the lessons that he was taught during his personal war at Devon. The theme of maturity in A Separate Peace can be reflected from the pressures that Gene (and the others students) endures during the drills, labors, and strict rules at Devon; this can be categorized as the â€Å"external† features of the theme, as well as the background of the novel. However, the theme of coming to age can also be seen in Gene’s heart, as he participates in an emotional struggle within himself prior to Finny’s character. We can see that Gene becomes jealous and envious of Finny, but then there seems to be a development in his character as he slowly begins to realize the truth. In a sense, Gene reaches maturity and becomes an adult after Finny dies, as he realizes that his own enemy was not Finny but his ignorant heart. Both the external and internal features of maturity in this novel gives meaning to the phrase, â€Å"Innocence must be killed to give birth to experience†; the students had to leave their peaceful state in the summer of 1942 and began to get used to the rigors of war and labor to fully understand the realities of war; at the same time, if Finny had not suffered and eventually died in the end, Gene would not have reflected on himself and grow from his experiences in the past. As Gene discovered in the end, true identity can only be reached through a state of maturity. â€Å"A Separate Peace† By John Knowles Essay In â€Å"A Separate Peace† by John Knowles, it is evident that Finny and Leper undergo the most traumatic experiences from the Class of 1943. Through these experiences, both characters lose much of their innocence and naivety. Finny, upon learning of the existence of the war and Gene’s moment of hatred, learns to accept realities and perceive the world as it is, not as the perfect childlike image he wants it to be. However, when Leper enlists in the army, he quickly begins to have hallucinations because the reality is too much for him to handle. Nevertheless, he eventually overcomes his insanity and seems to be fairly mentally stable by the end of the novel. Although Finny and Leper’s traumas are the source of a major loss of purity and childhood, they are also the cause of post-tramautic growth and a necessary increase in maturity. Finny goes through several perception-changing events during the course of the novel, but the event that cements his departure from childhood is the acceptance that Gene deliberately shook Finny off the tree. This shock was caused by his own inability to accept the truth in the first place. Despite the ease of denying unwanted information and living in a dream world, it is mentally unhealthy for Finny because of the shock caused upon finally believing the truth. Immediately after Gene’s confession of jouncing the limb, Gene remarks that Finny looked â€Å"older than I had ever seen him† (62). Finny, however, does not yet comprehend feelings of jealousy and betrayal, as he has hardly had any himself and finds it difficult to think of another’s point of view; the information registers on his face, but before he has time to process it and mature he rejects the idea entirely. Gene states â€Å"it occurred to me that this could be an even deeper injury than what I ha d done before† (62). The reality of adult themes such as jealousy, betrayal, and hate is what hurts Finny most, not the crippling injury itself. Another reality that takes away from Finny’s nescience is the war (when he finally believes in its existence). The most dramatic and stunning war in recent history, World War II had a huge impact on millions of lives worldwide. Yet Phineas refused to believe in the war, and instead created a fantasy in which he was the one of the only people who knew that it was all a hoax. When Gene, in disbelief from Finny’s opinion, questions Finny on why he is the only person who is aware of the â€Å"stuffed shirts'† (107) plot to  suppress happiness, Finny emotionally bursts out it is because he has â€Å"suffered† (108). Apparently, Finny has visualized this hoax to shield himself from the disadvantages of his disability, such as enlisting. Nevertheless, Finny quickly accepts the truth of the war after seeing Leper in a mentally disturbed state of mind. The image of what the war did to someone who used to be close to him shook him out of his dream world and spurred his emotional growth. When Finny, at the end of the novel, learned to accept the realities and avoid using denial to cope with shock, he lost the last of his childhood innocence. Leper is easily one of the most naive and innocent characters during the Summer Session. His good-naturedness and passive fascination with nature is such an ideal image of innocence that it seems almost depressing to see him in the traumatized state of mind after enlisting. Even while everyone is volunteering to shovel snow to aid the war effort and discussing their plans for which division to enlist in, Leper is only concerned with the beauty of nature and skis to a beaver dam to watch the beavers develop and build their dam. He is moved to join the army not for vain images of glory and glamor like the other students, but rather for the beauty of skiing down a mountain. Obviously, he soon finds that the army is too much for him, and while absent from the ongoings at Devon he loses every shred of innocence and guilelessness that previously surrounded his character. When Gene meets him, his psyche is obviously changed to such a point that he has hallucinations and other symptoms of sc hizophrenia, caused by his rapid ascension into adult matters. He does not accept reality nearly as well as Finny does because his character was far more innocuous at the start of the novel. So many of his images of the world are shattered that it can be seen that he feels like he has little familiarity to hold onto. He grasps to every gleam of regularity and unchangeable function, which explains his preference for spending time in the dining room of his house simply because he knows that three daily meals will be served there on a consistent basis. However, his time at home seems to have given him time to cope with the images of adulthood. Upon his return to Devon, he seems mentally well and a much more decisive authority than ever before. He accurately and forcefully convicts Gene of jouncing the limb in â€Å"his new, confident†¦ voice† (166).  Gene describes Leper during the trial as â€Å"all energy† (165). Evidently, Leper has dealt with the loss of innocence caused by his abrupt initiation into adulthood and has becom e a more confident, self-assured person in spite of it. Knowles makes it apparent throughout A Separate Peace that while the loss of innocence may often seem to be a sad or tragic event, it is necessary to pave the way for maturation and a transition into adulthood. Had Finny never accepted the truth of the tragedy that occurred to him, he would have never matured beyond his carefree summer days. And had Leper kept living in his own world of vivid imaginations, he would have never developed into the sanguine individual he becomes at the end of the novel. While the loss of innocence is partly a lugubrious experience, John Knowles portrays it as a necessity – a part of maturation and growth that leads to adulthood and self-fulfillment.

Advantages of Computer Essay

Agriculture is a vital sector of Pakistan’s economy and accounted for almost 30 percent of GDP annually, according to government estimates. The sector directly supports three-quarters of the country’s population, employs half the labor force, and contributes a large share of foreign exchange earnings. The main agricultural products are cotton, wheat, rice, sugarcane, fruits, and vegetables, in addition to milk, beef, mutton, and eggs. Pakistan depends on one of the world’s largest irrigation systems to support production. The following are the main crops cultivated in Pakistan: Wheat: Wheat is a staple food used in manufacture of baked products. It is grown on Barani lands. Wheat is grown in Punjab, Sindh and some parts of K.P.K for cultivation of wheat. The temperature is favorable from October to May for the production of wheat. It does not need a lot of water. Pakistan is not self sufficient in wheat production and has to import wheat from foreign countries. It accounts for over 70% of gross cereals and over 36% of the country’s acreage is devoted to wheat cultivation. Rice: Rice is a Kharif crop and needs a great deal of water and heat. It is known as â€Å"crop of water†. It is grown in Punjab and Sindh. North-eastern Punjab and Larkana district are main rice growing regions. The Irri, Basmati and desi varieties are grown in Pakistan. Basmati is the most famous variety of rice grown in Pakistan. Its highest acreage is in the north eastern part of Pakistan. Pakistan is the world’s fourteenth largest producer of rice. Pakistan produces about 6 million tons of rice a year. Sugar Cane Sugarcane is included in both Rabi and Kharif Crops. It is an important cash crop of Pakistan. It is a type of long grass â€Å"perennial† in nature. It is the most important and cheapest source of refined sugar. Gur,Alcohol and Desi Shakkar are also prepared from Sugar cane.The left out stalk fibers (bagasse) are used in the paper industries. It is cultivated in the spring season and harvested in November-December. It is mostly cultivated in canal irrigated areas of Punjab, KPK and Sindh provinces. Cotton Cotton also known as the â€Å"Silver Fiber† is the most important cash crop of Pakistan. It is known to have been produced in the Indus plain since 3000 BC. Pakistan, ranks fifth in world cotton production and earns a large amount of foreign exchange from its export. It accounts for approximately one half of the all materials that are made into cloth and provides employment to 2/3rd of industrial labour force Cotton is a Kharif crop and is grown in canal irrigated areas of Punjab and Sindh and also in some parts of Baluchistan and KPK.

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New Media and Consumer Behavior - Essay Example However, the dynamics associated with social and new media also present new opportunities for marketers to begin changing the expectations with marketing. This development is one that is causing marketers to reexamine their approach to new media as well as the associations that are a part of the behaviors which are being created. Social Media and Marketing The development of social media and the use of new media have allowed marketers to take a completely new approach to the brands that are developed. This is based on the understanding that the consumer responses hold more weight than before. More important, the social media brand name which is communicated to others creates direct responses from those that are working within the market. For this to work correctly, a hybrid model of the promotional mix is used. This is based on developing network platforms and promotional tools that engage customers. The risk within this comes from the responses from consumer behavior and the open ne twork which often leads to a loss of control over the brand name. If a consumer has a bad review of the company and other problems occur, then there is a lack of development and understanding with the social media. This response is furthered with the communication that is now open and used for promotions in the marketplace. These responses have developed into a different understanding and development of marketing communication in the marketplace (Mangold, Faulds, 2009:51). The social and new media created then leads into a mixture of promotions which works as a medium for marketers. The medium; however, includes specialized dynamics that alter the way in which one is working within the social media. This is combined with the technological models and advertising that is within the virtual world. The main approach which needs to be created is to allow the consumers to communicate the main message which helps to build the brand identity of the products used. The main approach then furt hers into the purchasing intention that is required with the products developed as well as the way in which this presentation is offered in virtual worlds. The challenge with the new medium becomes based on the communication levels which are used and the way in which the promotional platforms hold specific dynamics. As these dynamics alter, there is a specific association with how these work in terms of purchasing intention and development. The medium that is established then focuses on connecting the right customers and monitoring ways for the new media to promote what is needed in terms of communication for a specific product (Barnes, 2007: 13). Understanding Consumer Behavior The main alteration which has occurred with the social media portals is the consumer behavior that is a part of the social media. There is a specific culture which has influenced the approach toward specific brand names and the results which have become a part of this. The culture which is associated with th is is developed with the understanding of global advertising and marketing that creates the culture and understanding. The consumer behavior for social media is to gain information, specifically from influences of peer groups. Word of mouth that is a part of the global culture and the associations which are related to the business and media then develop a different under

General Mill Essay Example | Topics and Well Written Essays - 1000 words

General Mill - Essay Example Besides that, the company has its joint ventures manufacturing and marketing products in over 130 countries in addition to republics globally. Its operations are categorized into three- Foodservice and baking, International- excludes their partnership with Japan and Cereal Partners Worldwide, CPW and U.S Retail- consisting of seven divisions of the branded retail products; its joint venture with CPW sees them selling ready-to-eat cereals. Overview Recently, the Company made an acquirement of a natural snacks food corporation known as Food Should Taste Good that is located within Needham Heights. Happening within the same year, 2012, "the Company acquired a 50% interest and a 51% interest in Yoplait Marques S.A.S and Yoplait S.A.S. respectively, and later on, during August, it obtained Yoki Alimentos SA" (Reuters, n.d). Following its mission ‘Nourishing Lives’, the Company has its healthy snacks brands such as Nature Valley bars and Yoplait dairy products still having a l arge consumer base worldwide. Most notable is its Gold Medal flour that, till today, has remained the number one selling flour in the United States. The Company also participates in philanthropic efforts through its General Mills Foundation, where "more than half a million dollars has been awarded to nonprofit organizations supporting local communities since 1954" (General Mills -foundation, n.d). Furthermore, it has remained consistent in paying dividends to its investors. Coming after the Kellog Company, General Mills is the number two cereals maker in America, in terms of size. General Mills employs an estimated 35,000 employees across its network in Asia Pacific, Europe, Canada, Latin America and South Africa, its manufacturing companies are situated in beyond 30 countries. Products The Company can be said to have had great success because of its consumer-relating brands on all its products. Other than the Gold Medal flour, its breakfast products fetch a considerable consumer ba se and they include "Cheerios, Lucky Charms, Chex, Wheaties and Trix" (General mills- cereals n.d). The lineup continues on with baking mixes under the name Betty Crocker, frozen bread called Pilsbury and yoghurt called Yoplait and Colombo, the Company also makes vegetables that are frozen and preserved named Green Giant, Progresso soups and Mexican seasonings. General Mills has a natural as well as organic products venture named Small Planet Foods that is responsible for marketing Cascadian Farm vegetables plus Muir Glen soups. In addition, it has a joint venture with 8th venture for marketing and selling soy-based products; where that company has a 50-50 partnership with Dupont. Operations Owing to the fact that General Mills’ has a diverse product range where each may have similar or different specifications, the company took up professional IT services from a Management Information Systems company to design a feature in their product specifications system. This feature wo uld make it possible to conduct mass changes and undo designs of the products. The designing entailed rigorous testing with employees from its U.S and abroad operations also participating. This helped the Company to save time and minimize errors and subsequently, maintain consistent levels of quality in their products, their packaging and delivery to various parts of the world; while at the same time adhering to strict regulations. The system has the added advantage of allowing the Company to make more than 10,000 modifications

The Iran- Iraq War Research Paper Example | Topics and Well Written Essays - 1500 words

The Iran- Iraq War - Research Paper Example The Iran- Iraq War The United States covertly supported Iraq, even as they had arm dealings with the Iranian government. However, by the end of the war, neither Iran and Iraq, nor the United States got any benefits from this war. This paper aims at analyzing this war, and discussing the United State’s involvement and role in this war. Saddam Hussein had steadily risen to become Iraq’s most powerful leader by the mid 1970s (Yetiv 79). In 1979, he forced the country’s incumbent leaders to step down, and hold a meeting of the Ba’ath party’s leadership, where he arrested and executed his imagined and key political opponents. His position as Iraq’s dictator was an insecure one in 1980 since he had just grabbed power in bloodthirsty fashion. He, however, did not consider himself one of the thug-life dictators who were in power in most third world countries. He saw himself as an enlightened and modern leader whose purpose was to make Iraq the Middle East’s leading country. While he would sometime lean on religious rhetoric, especially in his battles with the US in later years, his regime was secular (Yetiv 80). His party’s ideology grounded itself on socialism rather than Islamic teachings. His government took to many reforms, including the secularization of Iraq’s legal code, which was opposed by a majority of the Iraqi’s on grounds of religion. While Saddam and his men were Sunni Muslims, a vast majority of Iraqi’s were Shia’s, which bothered Saddam who distrusted their loyalty.

The Ganges River Essay Example for Free

The Ganges River Essay The Ganges River is very sacred to the Hindus. This river starts in the Himalayas and flows across the northern part of India and into Bangladesh and finally empties into the Bay of Bengal. The Ganges River flows a total of 1,557 miles and provides water for southern Tibet, northern India, Nepal, and Bangladesh. This is a very important river for everyone, especially the Hindus, but f we do not take care of it will be destroyed by pollution. The Hindus use the Ganges River to cleanse their sins. They believe that the goddess Ganga came to earth in the form of a river, and if they bathe in it their sins will be cleansed. Millions of Hindus come long ways to cleanse themselves in the river each year. The Hindus believe that if you die while in the river, you are guaranteed a place in paradise. This river also provides nutrients for the farms it passes through. The tributaries  of the Ganges supports about 300 million people. Many of these people do not even know that their river is being polluted. The Ganges River runs along a few big cities. Factories in this city along the coast are spilling pollutants into the river. With the growing population, pollution is not slowing down. It is estimated that 230 million gallons of sewage are being put into the river each day. This river is becoming more polluted as the population gets larger and the amount of waste increases. If a very strong effort is not put in to helping save this river it will not be able to help anybody. Some efforts are being made to clean up the Ganges river. In 1986, a project for 250 million dollars was made to try to clean up the river. To deal with the population problem, incentives are given to families with two children or less. Still the average family has three to four children. More of an effort to restore this river needs to be made or else it will be destroyed. If this river does get destroyed it will devastate many people, especially the Hindus. Some people believe it is already to polluted to clean up. Better ways of controlling their pollution will need to be made to save this river.

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