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Reality Check for Data Mining
October 1996 (vol. 11 no. 5)
pp. 26-33

TO COMPETE EFFECTIVELY IN today's marketplace, business managers must take timely advantage of high-return opportunities. Doing so requires that they be able to exploit the mountains of data their organizations generate and collect during daily operations. Yet, the difficulty of discerning the value in that information--of separating the wheat from the chaff--prevents many companies from fully capitalizing on the wealth of data at their disposal.

For example, a bank account manager might want to identify a group of married, two-income, affluent customers and send them information about the bank's growth mutual funds, before a competing discount broker can lure them away. The information surely resides in the bank's computer system--and has probably been there in some form for years. The trick, of course, is to find an efficient way to extract and apply it.

Data mining -- the process of extracting valid, previously unknown, comprehensible, and actionable information from large databases and using it to make crucial business decisions--currently performs this task for a growing range of businesses. After presenting an overview of current data-mining techniques, this article explores two particularly noteworthy applications of those techniques: market basket analysis and customer segmentation.

Citation:
Evangelos Simoudis, "Reality Check for Data Mining," IEEE Intelligent Systems, vol. 11, no. 5, pp. 26-33, Oct. 1996, doi:10.1109/64.539014
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