Jamie MacLennan, ZhaoHui Tang, Bogdan Crivat

Data Mining with Microsoft SQL Server 2008

• Broschiertes Buch

Produktbeschreibung

Understand how to use the new features of Microsoft SQL Server 2008 for data mining by using the tools in Data Mining with Microsoft SQL Server 2008 , which will show you how to use the SQL Server Data Mining Toolset with Office 2007 to mine and analyze data.

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Eine praxisorientierte Einführung in das Data Mining Toolset des SQL Server 2008 und die neuen Data Mining Add-Ins für Office 2007.

Enthält detaillierte Erläuterungen und Beispiele zu allen neuen Data Mining Features des SQL Server 2008.

Gibt präzise Anleitungen zum Arbeiten mit den wichtigsten Data Mining-Algorithmen, (Naive Bayes-, Decision Trees-, Time Series-, Sequence Clustering-, Association- und Neural Network-Algorithmus), zum Data Mining in OLAP Datenbanken und mit SQL Server Integration Services 2008.

Die begleitende Website enthält den kompletten Quellcode zu den Beispielen aus dem Buch.

Produktdetails

• Verlag: Wiley & Sons
• Artikelnr. des Verlages: 14527774000
• 1. Auflage
• Seitenzahl: 672
• Erscheinungstermin: 1. August 2008
• Englisch
• Abmessung: 235mm x 191mm x 36mm
• Gewicht: 950g
• ISBN-13: 9780470277744
• ISBN-10: 0470277742
• Artikelnr.: 23594524

Autorenporträt

Jamie MacLennan is principal development manager of the SQL Server Analysis Services at Microsoft. He has more than 25 patents or patents pending for his work on SQL Server Data Mining, and has written extensively on the data mining technology in SQL Server. ZhaoHui Tang is a principal group program manager at Microsoft adCenter and inventor of Keyword Services Platform. Bogdan Crivat is a senior software design engineer in SQL Server Analysis Services at Microsoft, working primarily on the data mining platform.

Inhaltsangabe

1. Introduction to Data Mining. 2. Applied Data Mining Using Microsoft
Excel 2007. 3. DMX and SQL Server Data Mining Concepts. 4. Using SQL Server
Data Mining. 5. Implementing a Data Mining Process Using Office 2007. 6.
Microsoft Naïve Bayes. 7. Microsoft Decision Trees Algorithm. 8. Microsoft
Time Series Algorithm. 9. Microsoft Clustering. 10. Microsoft Sequence
Clustering. 11. Microsoft Association Rules. 12. Microsoft Neural Network
and Logistic Regression. 13. Mining OLAP Cubes. 14. Data Mining with SQL
Server Integration Services. 15. SQL Server Data Mining Architecture. 16.
Programming SQL Server Data Mining. 17. Extending SQL Server Data Mining.
18. Implementing a Web Cross-Selling Application. 19. Conclusion and
Additional Resources. Appendix A. Datasets. Appendix B. Supported
Functions. Index.