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Data mining with Microsoft SQL server 2008 /
"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 mini...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Indianapolis, IN :
Wiley Pub.,
©2009.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a MacLennan, Jamie. | |
| 245 | 1 | 0 | |a Data mining with Microsoft SQL server 2008 / |c Jamie MacLennan, ZhaoHui Tang, Bogdan Crivat. |
| 260 | |a Indianapolis, IN : |b Wiley Pub., |c ©2009. | ||
| 300 | |a 1 online resource (xxxvi, 636 pages) : |b illustrations | ||
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| 500 | |a Includes index. | ||
| 520 | |a "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"--Resource description page | ||
| 505 | 0 | |a Ch. 1. Introduction to Data Mining in SQL Server 2008 -- Ch. 2. Applied Data Mining Using Microsoft Excel 2007 -- Ch. 3. Data Mining Concepts and DMX -- Ch. 4. Using SQL Server Data Mining -- Ch. 5. Implementing a Data Mining Process Using Office 2007 -- Ch. 6. Microsoft Naive Bayes -- Ch. 7. Microsoft Decision Trees Algorithm -- Ch. 8. Microsoft Time Series Algorithm -- Ch. 9. Microsoft Clustering -- Ch. 10. Microsoft Sequence Clustering -- Ch. 11. Microsoft Association Rules -- Ch. 12. Microsoft Neural Network and Logistic Regression -- Ch. 13. Mining OLAP Cubes -- Ch. 14. Data Mining with SQL Server Integration Services -- Ch. 15. SQL Server Data Mining Architecture -- Ch. 16. Programming SQL Server Data Mining -- Ch. 17. Extending SQL Server Data Mining -- Ch. 18. Implementing a Web Cross-Selling Application -- Ch. 19. Conclusion and Additional Resources -- Appendix A. Data Sets -- Appendix B. Supported Functions. | |
| 588 | 0 | |a Print version record. | |
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| 650 | 0 | |a Data mining. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 7 | |a COMPUTERS |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a Data mining. |2 blmlsh | |
| 650 | 7 | |a Data mining. |2 fast |0 (OCoLC)fst00887946 | |
| 700 | 1 | |a Tang, Zhaohui. | |
| 700 | 1 | |a Crivat, Bogdan. | |
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