IN THIS CHAPTER
Overview of the data mining process
Creating mining structures and models
Evaluating model accuracy
Deploying data mining functionality in applications
Mining algorithms and viewers
Mining integration with Analysis Services Cubes
Many business questions can be answered directly by querying a database—for example, "What is the most popular page on our website?" or "Who are our top customers?" Other, often more important, questions require deeper exploration—for example, the most popular paths through the website or common characteristics of top customers. Data mining provides the tools to answer such non-obvious questions.
The term data mining has suffered from a great deal of misuse. One favorite anecdote is the marketing person who intended to "mine" data in a spreadsheet by staring at it until inspiration struck. In this book, data mining is not something performed by intuition, direct query, or simple statistics. Instead, it is the algorithmic discovery of non-obvious information from large quantities of data.
Analysis Services implements algorithms to extract information addressing several categories of questions:
Segmentation: Groups items with similar characteristics. For example, develop profiles of top customers or spot suspect values on a data entry page.
Classification: Places items into categories. For example, determine which customers are likely to respond to a marketing campaign or which e-mails are likely to be spam. ...