ZhaoHui Tang / Jamie MacLennan

Data Mining with SQL Server 2005 (tentative)

• Broschiertes Buch

Produktbeschreibung

* Written by Microsoft's lead developers of SQL Server 2005 data mining technologies, this authoritative book is the only one of its kind to explain the new Microsoft data mining standard and cover the complete set of Microsoft data mining algorithms, such as decision trees, clustering, association rules, and time series
- The authors begin with a simple example of the data mining features of SQL Server 2005, then move on to provide an in-depth description of data mining components, focusing on OLE DB
- Timed to publish with the software release, the book provides code-level tutorials on how to use SQL Server 2005 to solve real-world business problems, including building a cross-sales
- Web application, forecasting using Excel, creating a targeted mailing campaign, and predicting Web usage

Produktdetails

• Verlag: John Wiley & Sons
• Englisch
• ISBN-13: 9780471462613
• ISBN-10: 0471462616
• Artikelnr.: 14843865

Autorenporträt

ZhaoHui Tang is a Lead Program Manager in the Microsoft SQL Server Data Mining team. Joining Microsoft in 1999, he has been working on designing the data mining features of SQL Server 2000 and SQL Server 2005. He has spoken in many academic and industrial conferences including VLDB, KDD, TechED, PASS, etc. He has published a number of articles for database and data mining journals. Prior to Microsoft, he worked as a researcher at INRIA and Prism lab in Paris and led a team performing data-mining projects at Sema Group. He got his Ph.D. from the University of Versailles, France in 1996.

Jamie MacLennan is the Development Lead for the Data Mining Engine in SQL Server. He has been designing and implementing data mining functionality in collaboration with Microsoft Research since he joined Microsoft in 1999. In addition to developing the product, he regularly speaks on data mining at conferences worldwide, writes papers and articles about SQL Server Data Mining, and maintains data mining community sites. Prior to joining Microsoft, Jamie worked at Landmark Graphics, Inc. (division of Halliburton) on oil gas exploration software and at Micrografx, Inc. on flowcharting and presentation graphics software. He studied undergraduate computer science at Cornell University.

Inhaltsangabe

About the Authors.
Credits.
Foreword.
Chapter 1: Introduction to Data Mining.
Chapter 2: OLE DB for Data Mining.
Chapter 3: Using SQL Server Data Mining.
Chapter 4: Microsoft Naïve Bayes.
Chapter 5: Microsoft Decision Trees.
Chapter 6: Microsoft Time Series.
Chapter 7: Microsoft Clustering.
Chapter 8: Microsoft Sequence Clustering.
Chapter 9: Microsoft Association Rules.
Chapter 10: Microsoft Neural Network.
Chapter 11: Mining OLAP Cubes.
Chapter 12: Data Mining with SQL Server Integration Services.
Chapter 13: SQL Server Data Mining Architecture.
Chapter 14: Programming SQL Server Data Mining.
Chapter 15: Implementing a Web Cross-Selling Application.
Chapter 16: Advanced Forecasting Using Microsoft Excel.
Chapter 17: Extending SQL Server Data Mining.
Chapter 18: Conclusion and Additional Resources.
Appendix A: Importing Datasets.
Appendix B: Supported VBA and Excel Functions.
Index.