Data mining techniques data mining adopts several

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Unformatted text preview: k's customer database into several groups based on similarities of their age, sex, income, and residence. Once clustering is done, the bank management can examine the summarized characteristics of each subgroup, extract significant statistics, and use them to understand its customers better and thus provide more suitable products and customized services. Clustering -algorithms have also been successfully applied in the area of text mining for problems such as document indexing and topic identification in the research setting. Association The objective of association algorithms for data mining is to automatically discover association rules hidden in a database. An association rule reveals the associative relationships among objects. For example, an association rule may reveal that "If some object X is part of a transaction, then for some percent of such transactions, object Y is also part of the transaction." Thus association algorithms help in the discovery of togetherness, or the connection of objects. They are useful in solving problems where it is important to understand the extent to which the presence of some variables imply the presence of other variables and the prevalence of this pattern in the entire database under examination. An interesting and very useful application of association algorithm is marketbasket analysis, in which database of sales transactions are examined to extract patterns that identify what items sell together, what items sell better when relocated to new areas, and what product groupings improve department sales. For example, a retail store may discover that people tend to buy soft drinks and potato chips together. Store personnel then place the potato chips near the soft drinks to promote the sale of both. They may even discount one to entice buying the other, since these buyers now will be saving some money. Another useful application of association algorithm is the analysis of Internet traffic on a web site to better understand where the real demand is, what pages are being looked collectively, and so on...
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This document was uploaded on 04/07/2014.

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