IDMer (数据挖掘者)--Amazon数据挖掘图书推荐 (by DM professional)

	Amazon数据挖掘图书推荐 (by DM professional)

	数据挖掘者发表于 2005/6/25 17:10:56

	500)this.width=500'> My Favorite Data Mining Books: A list by Tom Breur, Data mining professional 1. Business Modeling and Data Mining by Dorian Pyle (Paperback - April 2003) Average Customer Review: *** Tom Breur's comments:By far the best data mining book I know. Great conceptual coverage of how explicating the problem and the business model define how the mining project should proceed. Comprehensive methodology. 2. Data Preparation for Data Mining by Dorian Pyle (Paperback) Average Customer Review: ^ Tom Breur's comments:Data preparation is where typically 80%+ of the projects time is spent. Cleaning and transforming the data in itself can provide invaluable insight. A classic, must have for serious miners. 3. Seven Methods for Transforming Corporate Data Into Business Intelligence by Vasant Dhar, et al Average Customer Review: ^ Tom Breur's comments:The first three chapters show a methodology for mapping data mining solutions onto problems. Although some of the other material is slightly dated, the first three chapters still hold their own. 4. Mastering Data Mining: The Art and Science of Customer Relationship Management by Michael J. A. Berry, Gordon S. Linoff (Paperback) Average Customer Review: Tom Breur's comments:Good introductory textbook. The first four chapters treat data mining and CRM, then four more with practical guidelines. Chapters 9-14 are somewhat fluffy case studies. 5. Data Mining Techniques : For Marketing, Sales, and Customer Support by Michael J. A. Berry, Gordon Linoff Average Customer Review: ^ Tom Breur's comments:Basic overview of most commonly used data mining algorithms, from a practitioners perspective. Has stood up the test of time remarkedly well. 6. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (Morgan Kaufmann Series in Data Management Systems) by Ian H. Witten, Eibe Frank (Paperback) Average Customer Review: * Tom Breur's comments:Limited to rules and trees only, the coverage of these subjects (with statistical foundation) is solid. Machine learning oriented. 7. Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management by Olivia Parr Rud (Paperback) Average Customer Review: Tom Breur's comments:Covers ONLY regression methods, using SAS. Within this limitation, it's a fine introduction (but no more that that) into this topic. Useful SAS example programs. 8. Machine Learning by Tom M. Mitchell (Hardcover) Average Customer Review: ^ Tom Breur's comments:Thorough coverage of both application issues, as well as the statistical core of most data mining algorithms. 9. Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg (Hardcover) Average Customer Review: ^ Tom Breur's comments:Great textbook on genetic algorithms, solid coverage. 10. Advances in Knowledge Discovery and Data Mining by Usama M. Fayyad, et al (Paperback) Average Customer Review: ***

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