Data Mining with SQL Server 2005 (PDF)
(Sprache: Englisch)
Your in-depth guide to using the new Microsoft data miningstandard to solve today's business problemsConcealed inside your data warehouse and data marts is a wealthof valuable information just waiting to be discovered. All you needare the right tools to...
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Your in-depth guide to using the new Microsoft data miningstandard to solve today's business problemsConcealed inside your data warehouse and data marts is a wealthof valuable information just waiting to be discovered. All you needare the right tools to extract that information and put it to use.Serving as your expert guide, this book shows you how to create andimplement data mining applications that will find the hiddenpatterns from your historical datasets. The authors explore thecore concepts of data mining as well as the latest trends. Theythen reveal the best practices in the field, utilizing theinnovative features of SQL Server 2005 so that you can beginbuilding your own successful data mining projects.You'll learn:* The principal concepts of data mining* How to work with the data mining algorithms included in SQLServer data mining* How to use DMX-the data mining query language* The XML for Analysis API* The architecture of the SQL Server 2005 data miningcomponent* How to extend the SQL Server 2005 data mining platform byplugging in your own algorithms* How to implement a data mining project using SQL ServerIntegration Services* How to mine an OLAP cube* How to build an online retail site with cross-sellingfeatures* How to access SQL Server 2005 data mining featuresprogrammatically
Inhaltsverzeichnis zu „Data Mining with SQL Server 2005 (PDF)“
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.
Autoren-Porträt von ZhaoHui Tang, Jamie MacLennan
ZhaoHui Tang is a Lead Program Manager in the Microsoft SQLServer Data Mining team. Joining Microsoft in 1999, he has beenworking on designing the data mining features of SQL Server 2000and SQL Server 2005. He has spoken in many academic and industrialconferences including VLDB, KDD, TechED, PASS, etc. He haspublished a number of articles for database and data miningjournals. Prior to Microsoft, he worked as a researcher at INRIAand Prism lab in Paris and led a team performing data-miningprojects at Sema Group. He got his Ph.D. from the University ofVersailles, France in 1996.Jamie MacLennan is the Development Lead for the DataMining Engine in SQL Server. He has been designing and implementingdata mining functionality in collaboration with Microsoft Researchsince he joined Microsoft in 1999. In addition to developing theproduct, he regularly speaks on data mining at conferencesworldwide, writes papers and articles about SQL Server Data Mining,and maintains data mining community sites. Prior to joiningMicrosoft, Jamie worked at Landmark Graphics, Inc. (division ofHalliburton) on oil & gas exploration software and atMicrografx, Inc. on flowcharting and presentation graphicssoftware. He studied undergraduate computer science at CornellUniversity.
Bibliographische Angaben
- Autoren: ZhaoHui Tang , Jamie MacLennan
- 2005, 1. Auflage, 460 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 0471754684
- ISBN-13: 9780471754688
- Erscheinungsdatum: 19.09.2005
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