Data mining, or knowledge discovery, is a process of discovering patterns that lead to actionable knowledge from large data sets through one or more traditional data mining techniques, such as market basket analysis and clustering. A lot of the knowledge discovery methodology has evolved from the combination of the worlds of statistics and computer science. Data mining focuses mostly on discovering knowledge in association with six basic tasks.

• Classification, which involves examining the attributes of a particular object and assigning it to a defined class. Classification can be used to divide a customer base into best, mediocre, and low-value customers, for instance, to distinguish suspicious characters at an airport security check, identify a fraudulent transaction, or identify prospects for a new service.

• Estimation, which is a process of assigning some continuously valued numeric value to an object. For example, credit risk assessment is not necessarily a yes/no question; it could be some kind of scoring that assesses a propensity to default on a loan. Estimation can be used as part of the classification process (such as using an estimation model to guess a person's annual salary as part of a market segmentation process).

• Prediction, which is an attempt to classify objects according to some expected future behavior. Classification and estimation can be used for prediction by applying historical data where the classification is already known to build a model (this is called training). That model can then be applied to new data to predict...