DATA MINING WITH MICROSOFT SQL SERVER 2008 1

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. Jamie Maclennan, Zhaohui Tang, Bogdan Crivat. Description Based On Print Version Record. Includes Index. Data Mining with Microsoft® SQL Server® 2008 About the Authors Credits Acknowledgments Contents at a Glance Contents Foreword Introduction How This Book Is Organized Who Should Read This Book Conventions Tools You Will Need What’s on the Website Chapter 1: Introduction to Data Mining in SQL Server 2008 Business Problems for Data Mining Data Mining Tasks Data Mining Project Cycle Summary Chapter 2: Applied Data Mining Using Microsoft Excel 2007 Setting Up the Table Analysis Tools The Analyze Key Influencers Tool The Detect Categories Tool The Fill From Example Tool The Forecasting Tool The Highlight Exceptions Tool The Scenario Analysis Tool The Prediction Calculator Tool The Shopping Basket Analysis Tool Technical Overview of the Table Analysis Tools Summary Chapter 3: Data Mining Concepts and DMX History of DMX Why DMX? The Data Mining Process Key Concepts DMX Objects DMX Query Syntax Prediction Summary Chapter 4: Using SQL Server Data Mining Introducing the Business Intelligence Development Studio Setting Up Your Data Sources Creating and Editing Models Processing Using Your Models Using SQL Server Management Studio Summary Chapter 5: Implementing a Data Mining Process Using Office 2007 Introducing the Data Mining Client Importing Data Using the Data Mining Client Data Exploration and Preparation Modeling Accuracy and Validation Model Usage Data Mining Cell Functions Model Management Trace Summary Chapter 6: Microsoft Naive Bayes Introducing the Naive Bayes Algorithm Using the Naive Bayes Algorithm Understanding Naive Bayes Principles Naive Bayes Parameters Summary Chapter 7: Microsoft Decision Trees Algorithm Introducing Decision Trees Using Decision Trees Decision Tree Principles Parameters Stored Procedures Summary Chapter 8: Microsoft Time Series Algorithm Overview Usage DMX Principles of Time Series Parameters Model Content Summary Chapter 9: Microsoft Clustering Overview Usage of Clustering Principles of Clustering Parameters Summary Chapter 10: Microsoft Sequence Clustering Introducing the Microsoft Sequence Clustering Algorithm Using the Microsoft Sequence Clustering Algorithm Microsoft Sequence Clustering Algorithm Principles Model Content Algorithm Parameters Summary Chapter 11: Microsoft Association Rules Introducing Microsoft Association Rules Using the Association Rules Algorithm Association Algorithm Principles Understanding Basic Association Algorithm Terms and Concepts Algorithm Parameters Summary Chapter 12: Microsoft Neural Network and Logistic Regression Same Principle, Two Algorithms Using the Microsoft Neural Network Model Content Interpreting the Model Principles of the Microsoft Neural Network Algorithm Nonlinearly Separable Classes Algorithm Parameters Summary Chapter 13: Mining OLAP Cubes Introducing OLAP Performing Calculations Browsing a Cube Understanding Unified Dimension Modeling Understanding the Relationship between OLAP and Data Mining Building OLAP Mining Models Using Wizards and Editors Understanding Data Mining Dimensions Using MDX within DMX Queries Using Analysis Management Objects for the OLAP Mining Model Summary Chapter 14: Data Mining with SQL Server Integration Services An Overview of SSIS Working with SSIS in Data Mining Summary Chapter 15: SQL Server Data Mining Architecture Introducing Analysis Services Architecture XML for Analysis Processing Architecture Predictions Data Mining Administration Summary Chapter 16: Programming SQL Server Data Mining Data Mining APIs Using Analysis Services APIs Using Microsoft. AnalysisServices to Create and Manage Mining Models Browsing and Querying Mining Models Stored Procedures Summary Chapter 17: Extending SQL Server Data Mining Plug-in Algorithms Data Mining Viewers Summary Chapter 18: Implementing a Web Cross-Selling Application Source Data Description Building Your Model Making Predictions Integrating Predictions with Web Applications Summary Chapter 19: Conclusion and Additional Resources Recapping the Highlights of SQL Server 2008 Data Mining Exploring New Data Mining Frontiers and Opportunities Further Reference Appendix A: Data Sets Appendix B: Supported Functions DMX Language Functions VBA Functions Excel Functions ASSprocs Stored Procedures Index 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. Jamie MacLennan is principal development manager of the SQL Server Analysis Services at Microsoft. He has more than 25 patents or patents pending for his work on SQL Server Data Mining, and has written extensively on the data mining technology in SQL Server. ZhaoHui Tang is a principal group program manager at Microsoft adCenter and inventor of Keyword Services Platform. Bogdan Crivat is a senior software design engineer in SQL Server Analysis Services at Microsoft, working primarily on the data mining platform