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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.
Posted January 27, 2015
This book is typical of titles confined to a single vendor's tool. The focus is on this tool's features, not data mining. Learning the principles of data mining first will make a book like this unnecessary, and help you avoid pitfalls not covered by books like this one.
If you want to understand data mining, I recommend titles such as:
"Data Mining: Practical Machine Learning Tools and Techniques, Second Edition", by Witten and Frank ISBN: 0120884070
"Data Mining, Second Edition : Concepts and Techniques, Second Edition", by Han and Kamber ISBN: 1558609016