Data Mining

From RapidWiki

Data mining (DM), also called Knowledge Discovery in Databases (KDD), is the process of automatically searching large volumes of data for patterns using tools such as classification, regression, association rule mining, clustering, etc.

Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, users have the ability to identify key attributes of business processes and target opportunities.

The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user. Predictive modeling (forecasting) provides predictions of future events and may be transparent and readable in some approaches (e.g. rule based systems), and opaque in others (e.g. neural networks, support vector machines).

Examples

A simple example of data mining, often called Market Basket Analysis, is used by retail sales. If a clothing store records the purchases of customers, a data mining system could identify those customers who favour silk shirts over cotton ones. Although some explanations of relationships may be difficult, taking advantage of it is easier.

The example above deals with association rules within transaction-based data. Not all data is transaction based, and logical or inexact rules may also be present within a database. In a manufacturing application an example of an inexact rule might be "73% of products which have a specific defect or problem will develop a secondary problem within the next 6 months".

Other examples include the prediction of process results, or the classification or segmentation of objects into (predefined) groups.