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Visual Numerics integration into SQL Server Data Mining

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Our friends at Visual Numerics, Inc have created an excellent white paper covering the creation of SQL Server Data Mining plug-in algorithms in C#.  In addition to documenting all of the architecture and concepts required, they have created a step-by-step tutorial demonstrating how to integrate their own K-Means clustering algorithm into SQL Server Data Mining using the C# interfaces.  The entire data mining team here in SQL Server reviewed the paper - we couldn't have done a better job ourselves!  Kudos to VNI for such an accomplishment!

The paper includes FULL SOURCE CODE for their plug-in algorithm and is available at https://www.vni.com/company/whitepapers/MicrosoftBIwithNumericalLibraries.pdf.  Of course you will need to get a copy of their IMSL C# Numerical Library, for which an eval copy is available upon request to VNI.

As a teaser, here is the TOC for their paper:

Audience ..................................................................................................... 4
Rationale ..................................................................................................... 4
Background ................................................................................................. 5
Plug‐in Architecture .................................................................................... 8
Managed Plug‐in Development .................................................................. 9
IMSL C# Library: ClusterKMeans Integration........................................... 9
Starting up.............................................................................................. 10
Metadata Changes (Metadata.cs) ......................................................... 10
Algorithm Changes (Algorithm.cs) ......................................................... 11
Training and Persistence of patterns..................................................... 11
Persistence of Patterns .......................................................................... 13
Prediction............................................................................................... 13
Algorithm Navigator Changes (AlgorithmNavigator.cs) ........................ 13
Registering the Algorithm with Analysis Services.................................. 14
Debugging .............................................................................................. 15
Other Default Features for Third‐Party Mining Algorithm Developers.... 16
The User Experience ................................................................................. 16
Excel 2007 .............................................................................................. 19
Conclusion................................................................................................. 21
About the Author...................................................................................... 21
References ................................................................................................ 22
Appendix A: Code Files ............................................................................. 23

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