Credit card companies have discovered another use for data mining. Before data mining, if you wanted to determine fraudulent transactions using a database you would query the database for all the transactions that had been determined fraudulent. You'd get back a few thousand records. Then you'd ask for all the transactions that were considered valid and you'd get ten or a hundred million. Your next task would be to stare at hundreds of thousands of variables and try to decide which of the variables predicted fraud.
Humans can handle two or three variables, but beyond that it's over.
Data mining algorithms structure the data and determine which attributes are relevant in a matter of minutes. That's all changed, due to the marriage of research and product groups at Microsoft. The data mining extensions in SQL Server 2000 will provide a common format for applications such as statistical analysis, pattern recognition, data prediction and segmentation methods, and visualization products. The data mining engine in SQL Server 2000 is a powerful platform. Though it ships with two algorithms, it is extensible and supports data mining algorithms that you might build.