Get a high-level overview of the benefits of the extensibility framework in SQL Server 2005 Analysis Services that allows independent software developers to easily integrate new data mining algorithms into the product.
This tip provides insight into the inner workings of the Microsoft Naive Bayes algorithm, showing how the algorithm computes the score used to filter out correlations.
This tip explores a DMX extension introduced in SQL Server 2005 SP2 that can be used to render lift reports directly in Reporting Services.
This tip shows you how to execute and use the results of prediction queries in multiple ways within the SQL Server relational database engine.
This article, published in the June 2005 issue of SIGMOD Record, provides an overview of SQL Server Data Mining from a standards perspective.
The two main functions of data mining are classification and prediction (or forecasting). Data mining helps you make sense of those countless gigabytes of raw data stored in databases by finding important patterns and rules present in the data or derived from it. Analysts then use this knowledge to make predictions and recommendations about new or future data. The main business applications of data mining are learning who your customers are and what they need, understanding where the sales are coming from and what factors affect them, fashioning marketing strategies, and predicting future business indicators.
An article from Microsoft Journal looking at the new Mining features of SQL Server 2005. If you're interested in Analysis Services, this ias a good look (from a high level) at the next version.