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Appendix 5: data mining.

Data Mining Methodology is a way of looking through large amounts of information to find particular bits of information. The "looking through" can be done by computer. Once the computer finds what it is looking for, it will sound an alarm or alert a human operator, who can then judge whether the item is what was wanted. Also useful is the fact that this methodology allows for a record of the search process, so that patterns of object or item occurrence can be stored and graphed. This pattern-creation is an aspect of the methodology that humans simply cannot perform on their own because of the very large amount of information that must be processed simultaneously.

The methodology has been developed largely by businesses to help with marketing, but it has also been useful to the medical profession and has real potential to law enforcement and intelligence operations.

Shaw, Michael J; Subramaniam, Chandrasekar; Tan, Gek Woo; Welge, Michael E. (2001). Knowledge management and data mining for marketing. Decision Support Systems. Vol. 31(1) 127-137.

Due to the proliferation of information systems and technology, businesses increasingly have the capability to accumulate huge amounts of customer data in large databases. However, much of the useful marketing insights into customer characteristics and their purchase patterns are largely hidden and untapped. A systematic methodology that uses data mining and knowledge management techniques is proposed to manage the marketing knowledge and support marketing decisions. This methodology can be the basis for enhancing customer relationship management.

Dennis, Charles; Marsland, David; Cockett, Tony. Data mining for shopping centers--Customer knowledge-management framework (2001). Journal of Knowledge Management. Vol. 5(4), 368-374.

The question was what specific attributes of shopping centers were most associated with spending for subgroups of shoppers. About 300 shoppers at six shopping centers were interviewed. They were asked for comparative ratings of the shopping center where the interview took place, as well as of the one where they shopped the most (or next most) for non-food shopping. Participants also rated the importance of 38 attributes, provided estimates of travel distance and time to each shopping center, and gave details such as monthly spending at each center. Conventional demographic variables were examined (females vs. males, upper vs. lower socioeconomic groups, higher vs. lower income groups, older vs. younger shoppers, and shoppers traveling by car vs. those traveling by public transport). Data mining (cluster analysis) identified two subgroups of consumers sharing particular needs and wants: those for whom service was important, and those for whom particular shops were important. These two subgroups differed in terms of high vs. low spending. These results demonstrate that data mining from a simple dataset can identify high-spending target consumers. Aspects of customer knowledge management for shopping centers are considered.
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Title Annotation:a way of looking through large amounts of information to find particular bits of information
Publication:Countering Terrorism: Integration of Practice and Theory
Geographic Code:1USA
Date:Feb 28, 2002
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