Data Mining with Microsoft SQL Server 2008
- 9h 42m
- Bogdan Crivat, Jamie MacLennan, ZhaoHui Tang
- John Wiley & Sons (US)
- 2009
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
About the Authors
Jamie MacLennan is the principal development manager of SQL Server Analysis Services at Microsoft. In addition to being responsible for the development and delivery of the Data Mining and OLAP technologies for SQL Server, MacLennan is a proud husband and father of four. He has more than 25 patents and patents pending for his work on SQL Server Data Mining. MacLennan has written extensively on the data mining technology in SQL Server, including many articles in MSDN Magazine, SQL Server Magazine, and postings on SQLServerDataMining.com and his blog at http://blogs.msdn.com/jamiemac. This is his second edition of Data Mining with SQL Server. MacLennan has been a featured and invited speaker at conferences worldwide, including Microsoft TechEd, Microsoft TechEd Europe, SQL PASS, the Knowledge Discovery and Data Mining (KDD) conference, the Americas Conference on Information Systems (AMCIS), and the Data Mining Cup conference.
ZhaoHui Tang is a group program manager at Microsoft adCenter Labs, where he manages a number of research projects related to paid search and content ads. He is the inventor of Microsoft Keyword Services Platform. Prior to adCenter, he spent six years as a lead program manager in the SQL Server Business Intelligence (BI) group, mainly focusing on data mining development. He has written numerous articles for both academic and industrial publications, such as The VLDB Journal and SQL Server Magazine. He is a frequent speaker at business intelligence conferences. He was also a co-author of the previous edition of this book, Data Mining with SQL Server 2005.
Bogdan Crivat is a senior software design engineer in SQL Server Analysis Services at Microsoft, working primarily on the Data Mining platform. Crivat has written various articles on data mining for MSDN Magazine and Access/VB/SQL Advisor Magazine, as well as numerous postings on the SQLServerDataMining.com website and on the MSDN Forums. He presented at various Microsoft and data mining professional conferences.
In this Book
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Foreword
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Introduction
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Introduction to Data Mining in SQL Server 2008
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Applied Data Mining Using Microsoft Excel 2007
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Data Mining Concepts and DMX
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Using SQL Server Data Mining
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Implementing a Data Mining Process Using Office 2007
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Microsoft Naïve Bayes
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Microsoft Decision Trees Algorithm
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Microsoft Time Series Algorithm
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Microsoft Clustering
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Microsoft Sequence Clustering
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Microsoft Association Rules
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Microsoft Neural Network and Logistic Regression
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Mining OLAP Cubes
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Data Mining with SQL Server Integration Services
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SQL Server Data Mining Architecture
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Programming SQL Server Data Mining
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Extending SQL Server Data Mining
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Implementing a Web Cross-Selling Application
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Conclusion and Additional Resources
