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blog名称:IDMer (数据挖掘者)
日志总数:175
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访问次数:2514280
建立时间:2005年6月24日 |
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“数据挖掘者”博客已经搬家，欢迎光临新博客网址：http://idmer.blog.sohu.com
我的新浪微博：@张磊IDMer |
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Amazon数据挖掘图书推荐 (by Research Manager) |
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数据挖掘者 发表于 2005/6/25 17:15:23 |
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My favourite data mining books: A list by vallaud, Research manager of arvato BI(18 item list)
1. Data Mining: Concepts and Techniques (Morgan Kaufmann Series in Data Management Systems) by Jiawei Han, Micheline Kamber (Hardcover - August 2000) Average Customer Review: ****^
2. Professional SQL Server 2000 DTS (Data Transformation Service) by Mark Chaffin, et al Average Customer Review: ***^
3. Common Warehouse Metamodel: An Introduction to the Standard for Data Warehouse Integration by John Poole, et al Average Customer Review: ****
4. Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications by Larissa T. Moss, Shaku Atre (Paperback) Average Customer Review: ****^
5. Data Preparation for Data Mining by Dorian Pyle (Paperback) Average Customer Review: ****^
6. Building and Managing the Meta Data Repository: A Full Lifecycle Guide by David Marco Average Customer Review: *****
7. Enterprise Knowledge Management: The Data Quality Approach by David Loshin (Paperback) Average Customer Review: *****
8. Constraint Processing by Rina Dechter (Hardcover - May 2003) Average Customer Review: *****
9. Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits by Larry P. English (Paperback) Average Customer Review: ****^
10. Data Quality: The Accuracy Dimension by Jack E. Olson (Paperback) Average Customer Review: *****
11. How to Build a Business Rules Engine : Extending Application Functionality through Metadata Engineering by Malcolm Chisholm (Paperback) Average Customer Review: *****
12. Business Modeling and Data Mining by Dorian Pyle (Paperback - April 2003) Average Customer Review: *****
13. Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management by Olivia Parr Rud (Paperback) Average Customer Review: ****
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