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Soft Computing

, Volume 13, Issue 3, pp 209–212 | Cite as

Evolutionary and metaheuristics based data mining

  • María J. del Jesús
  • José A. Gámez
  • José M. PuertaEmail author
Editorial

In a strict sense Data Mining (DM) is only one step of the Knowledge Discovery from Databases (KDD) process (Fayyad et al. 1996), concretely the phase consisting of the application of specific algorithms to extract patterns from data. However, the term Data Mining has been popularly used as a synonymous of KDD or at least in a wider sense that comprise the phases of data pre-processing and pattern/model discovering (or DM).

DM poses a great range of interesting (NP-hard) problems that consists mainly in searching for: the pattern/model that best describe the data, the more predictive subset of variables, the more accurate parameter configuration, etc. Because of this, and of the success obtained by metaheuristics when applied to other combinatorial/numerical optimization problems, metaheuristics have been widely applied to solve DM problems during the last years. In fact, the use of evolutionary algorithms and metaheuristics in general to approach DM-based problems (EMBDM) is a hot...

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • María J. del Jesús
    • 1
  • José A. Gámez
    • 2
  • José M. Puerta
    • 2
    Email author
  1. 1.Computer Science DepartmentUniversity of JaénJaénSpain
  2. 2.Computing Systems DepartmentUniversity of Castilla-La ManchaAlbaceteSpain

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