Data mining with microsoft SQL server 2008
MacLennan, Jamie.
Data mining with microsoft SQL server 2008 [electronic resource] / MacLennan, Jamie;Tang, ZhaoHui;Crivat, Bogdan. - WILEY, 2009. - 675 p.
Chapter 1: Introduction to Data Mining in SQL Server 2008. Chapter, Lesson, Part Chapter 2: Applied Data Mining Using Microsoft Excel 2007. Chapter, Lesson, Part Chapter 3: Data Mining Concepts and DMX. Chapter, Lesson, Part Chapter 4: Using SQL Server Data Mining. Chapter, Lesson, Part Chapter 5: Implementing a Data Mining Process Using Office 2007. Chapter, Lesson, Part Chapter 6: Microsoft Naive Bayes. Chapter, Lesson, Part Chapter 7: Microsoft Decision Trees Algorithm. Chapter, Lesson, Part Chapter 8: Microsoft Time Series Algorithm. Chapter, Lesson, Part Chapter 9: Microsoft Clustering. Chapter, Lesson, Part Chapter 10: Microsoft Sequence Clustering. Chapter, Lesson, Part Chapter 11: Microsoft Association Rules. Chapter, Lesson, Part Chapter 12: Microsoft Neural Network and Logistic Regression. Chapter, Lesson, Part Chapter 13: Mining OLAP Cubes. Chapter, Lesson, Part Chapter 14: Data Mining with SQL Server Integration Services. Chapter, Lesson, Part Chapter 15: SQL Server Data Mining Architecture. Chapter, Lesson, Part Chapter 16: Programming SQL Server Data Mining. Chapter, Lesson, Part Chapter 17: Extending SQL Server Data Mining. Chapter, Lesson, Part Chapter 18: Implementing a Web Cross-Selling Application. Chapter, Lesson, Part Chapter 19: Conclusion and Additional Resources. Chapter, Lesson, Part Cover, Title,Data mining with microsoft SQL server 2008--Preface, Introduction, TOC,About the Authors--Preface, Introduction, TOC,Credits--Preface, Introduction, TOC,Acknowledgments--Preface, Introduction, TOC,Contents at a Glance--Preface, Introduction, TOC,Contents--Preface, Introduction, TOC,Foreword--Preface, Introduction, TOC,Introduction--References, Appendix, Index,Appendix A: Data Sets--References, Appendix, Index,Appendix B: Supported Functions--References, Appendix, Index,Index.
9780470277744 9780470439296
Computers
Database management
General
Data mining with microsoft SQL server 2008 [electronic resource] / MacLennan, Jamie;Tang, ZhaoHui;Crivat, Bogdan. - WILEY, 2009. - 675 p.
Chapter 1: Introduction to Data Mining in SQL Server 2008. Chapter, Lesson, Part Chapter 2: Applied Data Mining Using Microsoft Excel 2007. Chapter, Lesson, Part Chapter 3: Data Mining Concepts and DMX. Chapter, Lesson, Part Chapter 4: Using SQL Server Data Mining. Chapter, Lesson, Part Chapter 5: Implementing a Data Mining Process Using Office 2007. Chapter, Lesson, Part Chapter 6: Microsoft Naive Bayes. Chapter, Lesson, Part Chapter 7: Microsoft Decision Trees Algorithm. Chapter, Lesson, Part Chapter 8: Microsoft Time Series Algorithm. Chapter, Lesson, Part Chapter 9: Microsoft Clustering. Chapter, Lesson, Part Chapter 10: Microsoft Sequence Clustering. Chapter, Lesson, Part Chapter 11: Microsoft Association Rules. Chapter, Lesson, Part Chapter 12: Microsoft Neural Network and Logistic Regression. Chapter, Lesson, Part Chapter 13: Mining OLAP Cubes. Chapter, Lesson, Part Chapter 14: Data Mining with SQL Server Integration Services. Chapter, Lesson, Part Chapter 15: SQL Server Data Mining Architecture. Chapter, Lesson, Part Chapter 16: Programming SQL Server Data Mining. Chapter, Lesson, Part Chapter 17: Extending SQL Server Data Mining. Chapter, Lesson, Part Chapter 18: Implementing a Web Cross-Selling Application. Chapter, Lesson, Part Chapter 19: Conclusion and Additional Resources. Chapter, Lesson, Part Cover, Title,Data mining with microsoft SQL server 2008--Preface, Introduction, TOC,About the Authors--Preface, Introduction, TOC,Credits--Preface, Introduction, TOC,Acknowledgments--Preface, Introduction, TOC,Contents at a Glance--Preface, Introduction, TOC,Contents--Preface, Introduction, TOC,Foreword--Preface, Introduction, TOC,Introduction--References, Appendix, Index,Appendix A: Data Sets--References, Appendix, Index,Appendix B: Supported Functions--References, Appendix, Index,Index.
9780470277744 9780470439296
Computers
Database management
General
