Data mining technology helps users analyze data in relational databases and multidimensional OLAP cubes to uncover patterns and trends that can be used to make predictions. The data mining capabilities in SQL Server 2000 are integrated tightly with both relational and OLAP data sources.

SQL Server 2000 includes two classes of data-mining algorithms developed by Microsoft Research, Microsoft Decision Trees and Microsoft Clustering. In addition, data mining in SQL Server 2000 supports algorithms developed by third parties.

• The Microsoft Decision Trees Algorithm. This algorithm is based on classification. The algorithm builds a decision tree that will predict the value of columns in a fact table, based upon other columns in the fact table. This algorithm might be used to identify individuals who are most likely to click on a particular banner ad or to buy a specific product from an e-commerce site.
• The Microsoft Clustering Algorithm. This algorithm groups records into clusters that exhibit some similar, predictable characteristics. Often, these characteristics may be hidden or nonintuitive. For example, the clustering algorithm might be used to group potential car buyers and determine how to create marketing campaigns that address each car-buying segment.