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Unformatted text preview: ISDS FINAL STUDY GUIDE Factors behind sudden popularity in data mining Reduction in cost of data storage and processing and increased hardware capacity have provided the ability to collect and accumulate data With increased database capacities and the availability of analysis tools, many companies recognized that they have untapped data and the tools to analyze Consolidation in a data warehouse, data both at the customer level and from various sources, gives the ability to analyze from a more complete view Examples of applications of data mining Identify success therapies for illness and to discover new drugs Reduce fraudulent behavior ( insurance claims and credit card usage) Identify customer buying patterns Reclaim profitable customers Aid in market-basket analysis Better target customers/clients Definition and Characteristics of Data Mining Data mining is used to describe knowledge discovery in databases Data Mining uses statistical, mathematical, and other techniques to extract and identify useful information and subsequent knowledge from large databases Data mining is also referred to as knowledge extraction, data archaeology, data exploration, data dredging, and information harvesting How Data Mining Works Data mining finds patterns and defines those patterns in terms of mathematical rules that can be used for prediction or association 4 Broad Categories for Data Mining Algorithms Classification Clustering Association Sequence Discovery Other Data Mining Procedures Linear regression analysis Time series Visualization DMAIC...
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This note was uploaded on 03/22/2011 for the course ISDS 2001 taught by Professor Herbert during the Spring '08 term at LSU.
- Spring '08