SQL data mining blog

Using Microsoft tools (such as SQL Server) for data mining has been briefly discussed on this blog. For more details, you may be interest in JamieMac's weblog. The posts concern data mining with a strong emphasis on Microsoft products. The author of this blog works for SQL data mining at Microsoft and is the co-author of SQL Server Data Mining (except an error from me). Among others are news about Office, addins for data mining, pointers to book chapters, etc. So if you're using a Microsoft product for your data mining tasks, you may be interested in this blog.

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Mining data with SQL Server (cont'd)

This post continues a previous one about Microsoft SQL Server and its possibilities for mining data. DMReview, a journal dealing with data mining in practice, has an interesting article about SQL Server. In the last section of this article, Donald Farmer (the author) writes more particularly about using SQL Server for mining data. This gives a good idea of possible applications for SQL Server in the field of data mining. However, some questions are not addressed in this particular article: What about data preparation? How to choose among different algorithms for a particular data mining task? How to deal with overfitting/underfitting?

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Mining data with Microsoft SQL Server

I have recently discussed with people working at Microsoft. Unfortunately, they are not doing data mining or machine learning research in Switzerland. I think that everything concerning research is in Redmond. I suppose that they are involved in data mining for at least two reasons: MSN Search and Microsoft SQL Server. The website IT-director has an article concerning SQL Server. According to its author, David Norris, data mining services in SQL Server are very interesting. He writes that "what Microsoft has done is to make data mining available on the desktop to everyone". However, no details are given in his article about data mining techniques used. Although I have never used SQL Server, I suspect that it is more about data analysis than really data mining.

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