Implementing Data Mining Algorithms in Microsoft SQL Server
Data mining technology is growing rapidly and, in many industries, has become commonplace. The main reasons for this are advances in computer technology that acquire, store and retrieve enormous amounts of data about everything from everywhere.In 2000, Microsoft introduced a data mining feature in their Microsoft? SQL Server? 2000 Analysis Services. This book covers all the practicalities required to integrate a third-party data mining algorithm into SQL Server 2000. This book is designed for use by data mining researchers and information technology workers. It will also be an ideal text for IT master and doctorate courses.
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Data mining technology overview
OLE DB for DM technology
Implementation of DMclcMine
Appendix B Building DMclcMine
Running the experiments
List of conventions
Appendix G List of Tables
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accuracy Appendix applications attribute ati BuysComputer C4.5 classifier class cj columns components computed continuous attributes create the DMM Cross-validation Data import workflow data mining model database DB for DM debug decision tree DM algorithms DM provider DMProv project dmreduce.cpp DMSample DMSQL statements DTS packages DTSBackup 2000 elapsed processing Execute experiment Figure file and insert file and replace folder function genetic algorithms implementation incremental input attributes Install Insurance Meteo Microsoft Microsoft Visual Studio MSDT classifier MSSQL Analysis Services MSVS number of prediction number of training OLAP OLE DB parameter Platform SDK PMML prediction attributes prediction tree Preprocessor probability of event processing workflow Program query rowset Sample script file shown in fig shown in listing shows SNBi classifier source code source files SQL Server standalone statements to create Table testing data set training data set UDA architecture values xi VarNumCases Visual Basic Visual C++ Visual Parse++ Waveform whole data