Data mining (also known as Knowledge Discovery in Databases - KDD) has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data"[1]

It uses machine learning, statistical and visualization techniques to discovery and present knowledge in a form which is easily comprehensible to humans.

[1] W. Frawley and G. Piatetsky-Shapiro and C. Matheus, Knowledge Discovery in Databases: An Overview. AI Magazine, Fall 1992, pgs 213-228.

See Also: Introduction To Data Mining

Data Mining evolved from a simple extraction of raw data to an analytical process of exploring large amount of data in order to cite the common denominators or patterns. Basically, data mining involves three processes:

Step 1: Exploration - involves data preparation

Step 2: Model building and Validation - involves choosing the ones that are best suited to be used

Step 3: Deployment - involves using the chosen data to proceed with the generation of the outcome

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