Synopses & Reviews
Your in-depth guide to using the new Microsoft data mining standard to solve today's business problems
Concealed inside your data warehouse and data marts is a wealth of valuable information just waiting to be discovered. All you need are the right tools to extract that information and put it to use. Serving as your expert guide, this book shows you how to create and implement data mining applications that will find the hidden patterns from your historical datasets. The authors explore the core concepts of data mining as well as the latest trends. They then reveal the best practices in the field, utilizing the innovative features of SQL Server 2005 so that you can begin building your own successful data mining projects.
- The principal concepts of data mining
- How to work with the data mining algorithms included in SQL Server data mining
- How to use DMX—the data mining query language
- The XML for Analysis API
- The architecture of the SQL Server 2005 data mining component
- How to extend the SQL Server 2005 data mining platform by plugging in your own algorithms
- How to implement a data mining project using SQL Server Integration Services
- How to mine an OLAP cube
- How to build an online retail site with cross-selling features
- How to access SQL Server 2005 data mining features programmatically
Data Mining with SQL Server Yukon shows database analysts and developers how to use all of the new features of Microsoft SQL Server Yukon for data mining. The book begins with a jump-start chapter, showing a simple example of the data mining features of SQL Server Yukon. The authors then provide an under-the-hood description of the data mining components of SQL Server Yukon, focusing on OLE DB for Data Mining. They show how to use each of the major data mining algorithms supported by this Microsoft tool, including decision trees, clustering, association rules, and time series. The authors also cover mining OLAP databases, as well as programming using ADO and stored procedures. The last set of chapters provide in-depth examples of using Microsoft data mining to solve four common types of business analysis problems., including:
o Building a cross-sales Web application.
o Forecasting using Excel.
o Creating a targeted mailing campaign.
o Predicting Web usage.
The companion Website will include the complete sample code and data sets provided in the book.
* Written by Microsoft's lead developers of SQL Server 2005 data mining technologies, this authoritative book is the only one of its kind to explain the new Microsoft data mining standard and cover the complete set of Microsoft data mining algorithms, such as decision trees, clustering, association rules, and time series
* The authors begin with a simple example of the data mining features of SQL Server 2005, then move on to provide an in-depth description of data mining components, focusing on OLE DB
* Timed to publish with the software release, the book provides code-level tutorials on how to use SQL Server 2005 to solve real-world business problems, including building a cross-sales
* Web application, forecasting using Excel, creating a targeted mailing campaign, and predicting Web usage
About the Author
is a Lead Program Manager in the Microsoft SQL Server Data Mining team. Joining Microsoft in 1999, he has been working on designing the data mining features of SQL Server 2000 and SQL Server 2005. He has spoken in many academic and industrial conferences including VLDB, KDD, TechED, PASS, etc. He has published a number of articles for database and data mining journals. Prior to Microsoft, he worked as a researcher at INRIA and Prism lab in Paris and led a team performing data-mining projects at Sema Group. He got his Ph.D. from the University of Versailles, France in 1996.
Jamie MacLennan is the Development Lead for the Data Mining Engine in SQL Server. He has been designing and implementing data mining functionality in collaboration with Microsoft Research since he joined Microsoft in 1999. In addition to developing the product, he regularly speaks on data mining at conferences worldwide, writes papers and articles about SQL Server Data Mining, and maintains data mining community sites. Prior to joining Microsoft, Jamie worked at Landmark Graphics, Inc. (division of Halliburton) on oil & gas exploration software and at Micrografx, Inc. on flowcharting and presentation graphics software. He studied undergraduate computer science at Cornell University.
Table of Contents
About the Authors.
Chapter 1: Introduction to Data Mining.
Chapter 2: OLE DB for Data Mining.
Chapter 3: Using SQL Server Data Mining.
Chapter 4: Microsoft Naïve Bayes.
Chapter 5: Microsoft Decision Trees.
Chapter 6: Microsoft Time Series.
Chapter 7: Microsoft Clustering.
Chapter 8: Microsoft Sequence Clustering.
Chapter 9: Microsoft Association Rules.
Chapter 10: Microsoft Neural Network.
Chapter 11: Mining OLAP Cubes.
Chapter 12: Data Mining with SQL Server Integration Services.
Chapter 13: SQL Server Data Mining Architecture.
Chapter 14: Programming SQL Server Data Mining.
Chapter 15: Implementing a Web Cross-Selling Application.
Chapter 16: Advanced Forecasting Using Microsoft Excel.
Chapter 17: Extending SQL Server Data Mining.
Chapter 18: Conclusion and Additional Resources.
Appendix A: Importing Datasets.
Appendix B: Supported VBA and Excel Functions.