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A guide to using the Microsoft(registered) data mining standard to solve business problems. This book shows how to create and implement data mining applications to find the hidden patterns from historical datasets. It explores the core concepts of data mining and reveals the best practices in the field, utilizing the features of SQL Server 2005.
List of contents
About the Authors.
Credits.
Foreword.
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.
Index.
