Data Mining with Microsoft SQL Server 2008
List price: $50.00
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Description: Data Mining with SQL Server 2008 shows database analysts, data miners, and developers how to use all of the new features of Microsoft SQL Server 2008 for data mining. The book begins with a quick overview of the SQL Server Data Mining Toolset, showing how these tools can be integrated with Office 2007 to provide a complete, user-friendly platform for mining and analyzing data. The authors next show how to use each of the major data mining algorithms supported by this Microsoft tool, including naive bayes, decision trees, time series, clustering, association rules, and neural networks. The authors also cover mining OLAP databases, as well as data mining with SQL Server Integration Services 2008. The last set of chapters provide in-depth examples of using Microsoft data mining to solve business analysis problems., including building a cross-sales Web application. The authors also cover significant new features for text mining. The companion Website will include the complete sample code and data sets provided in the book.
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
List price: $50.00
Copyright year: 2009
Publisher: John Wiley & Sons, Incorporated
Publication date: 11/17/2008
Size: 7.50" wide x 9.25" long x 1.50" tall
|Introduction to Data Mining|
|Applied Data Mining Using Microsoft Excel 2007|
|DMX and SQL Server Data Mining Concepts|
|Using SQL Server Data Mining|
|Implementing a Data Mining Process Using Office 2007|
|Microsoft Naï¿½ve Bayes|
|Microsoft Decision Trees Algorithm|
|Microsoft Time Series Algorithm|
|Microsoft Sequence Clustering|
|Microsoft Association Rules|
|Microsoft Neural Network and Logistic Regression|
|Mining OLAP Cubes|
|Data Mining with SQL Server Integration Services|
|SQL Server Data Mining Architecture|
|Programming SQL Server Data Mining|
|Extending SQL Server Data Mining|
|Implementing a Web Cross-Selling Application|
|Conclusion and Additional Resources|