People who want milk shouldn't sit on a stool in the middle of a field in the hopes that a cow will back up to them.
—Curtis Grant, Author
As the previous quote states so vividly, we should not sit around and wait for good things to drop into our milk pails. However, in Part IV of this book, we are talking about a technique that can, at first blush, sound like sitting and waiting for good things to drop out of your data and into your milk p … er, into your lap. We are talking about a technique called data mining.
In Chapter 2, we defined data mining as the use of a complex mathematical algorithm to sift through detail data to identify patterns, correlations, and clustering within the data. Does that mean we simply turn one of these complex mathematical algorithms loose on our data and nuggets of Business Intelligence (BI) wisdom will pour forth? Not exactly.
In this chapter, we learn more about what data mining is and how it is used. We look at the work we have to do to gain business intelligence using data mining and what things the data mining algorithms can do for us. Finally, we look at the seven data mining algorithms provided in SQL Server 2005 and discover what each one is good at.