Data Mining with Microsoft SQL Server 2008 [NOOK Book]

Overview

The most authoritative book on data mining with SQL Server 2008

SQL Server Data Mining has become the most widely deployed data mining server in the industry. Business users—and even academic and scientific users—have adopted SQL Server data mining because of its scalability, availability, extensive functionality, and ease of use.

The 2008 release of SQL Server brings exciting new advances in data mining. This authoritative and up-to-date ...

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Data Mining with Microsoft SQL Server 2008

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Overview

The most authoritative book on data mining with SQL Server 2008

SQL Server Data Mining has become the most widely deployed data mining server in the industry. Business users—and even academic and scientific users—have adopted SQL Server data mining because of its scalability, availability, extensive functionality, and ease of use.

The 2008 release of SQL Server brings exciting new advances in data mining. This authoritative and up-to-date resource shows how to master all of the latest features, with practical guidance on how to deploy and use SQL Server data mining for yourself.

The author team begins with an introduction to the tools, techniques, and concepts necessary to leverage SQL Server 2008 data mining. The discussion progresses to a thorough look at the details of the SQL Server 2008 data mining algorithms. You'll discover how to integrate SQL Server data mining into other parts of the SQL Server Business Intelligence (BI) suite and extend SQL Server data mining for your own needs. Detailed, practical examples clearly explain how to implement successful data mining solutions with SQL Server 2008.

Data Mining with Microsoft SQL Server 2008 shows you how to:

  • Apply data mining solutions using Microsoft Excel

  • Use the data mining Add-ins for Microsoft Office

  • Understand how, when, and where to apply the algorithms that are included with SQL Server data mining

  • Perform data mining on online analytical processing (OLAP) cubes

  • Extend SQL Server data mining by implementing your own data mining algorithms and stored procedures

  • Use SQL Server Management Studio to access and secure data mining objects

  • Use SQL Server Business Intelligence Development Studio to create and manage data mining projects

The companion website includes the complete sample code and data sets that are featured in the book.

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Product Details

  • ISBN-13: 9781118080009
  • Publisher: Wiley
  • Publication date: 3/10/2011
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 672
  • Sales rank: 919,229
  • File size: 21 MB
  • Note: This product may take a few minutes to download.

Meet 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.

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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.

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