The discovered patterns should be valid on new data with some degree of certainty. We also want patterns to be novel (at least to the system and preferably to the user) and potentially useful, that is, lead to some beneï¬t to the user or task. Finally, the patterns should be understandable, if not immediately then after some post processing (Fayyad et al, 1996).
Data mining is a step in the KDD process that consists of applying data analysis and discovery algorithms that, under acceptable computational efï¬ciency limitations, produce a particular enumeration of patterns (or models) over the data (Fayyad et al, 1996).
The KDD process involves using the database along with any required selection, pre- processing, subsampling, and transformations of it; applying data-mining methods (algorithms) to enumerate patterns from it; and evaluating the products of data mining to identify the subset of the enumerated patterns deemed knowledge. The data-mining component of the KDD process is concerned with the algorithmic means by which patterns are extracted and enumerated from data (Fayyad et al, 1996).

In more formal terms the entire KDD process can be deï¬ned as follows, given a set of facts F (Data), a language L, and some measure of certainty C, we deï¬ne a pattern as a statement S in L that describes relationships among a subset Fs of F with a certainty c, such that S is simpler (in some sense) than the enumeration of all facts in Fs (Frawley et al, 2000).
2.4.3 Data Mining
The objective of data mining is to identify valid novel, potentially useful, and understandable correlations and patterns in existing data [Chung and Gray 1999]. Finding useful patterns in data is known by different names (including data mining) in different communities (e.g. knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing) [Fayyad, et al, 1996].