Data Mining with Microsoft SQL Server 2008

by Jamie MacLennan, ZhaoHui Tang, Bogdan Crivat

Overview

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. Explore each of the major data mining algorithms, including naive bayes, decision trees, time series, clustering, association rules, and neural networks. Learn more about topics like mining OLAP databases, data mining with SQL Server Integration Services 2008, and using Microsoft data mining to solve business analysis problems.

Product Details

ISBN-13:	9781118080009
Publisher:	Wiley
Publication date:	03/10/2011
Sold by:	JOHN WILEY & SONS
Format:	eBook
Pages:	672
File size:	22 MB
Note:	This product may take a few minutes to download.

About the Author

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.

Table of Contents

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.