Greater knowledge extraction based on fuzzy logic and GKPFCM clustering algorithm

  • Authors:

  • Benjamín Ojeda-Magaña;Rubén Ruelas;Fulgencio S. Buendía Buendía;Diego Andina

  • Affiliations:

  • Departamento de Ingeniería de Proyectos, CUCEI, Universidad de Guadalajara, Zapopan, Jalisco, México;Departamento de Ingeniería de Proyectos, CUCEI, Universidad de Guadalajara, Zapopan, Jalisco, México;Departamento SSR, E.T.S.I Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, España;Departamento SSR, E.T.S.I Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, España

  • Venue:
  • CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
  • Year:
  • 2007

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Abstract

This work proposes how to generate a set of fuzzy rules from a data set using a clustering algorithm, the GKPFCM. If we recommend a number of clusters, the GKPFCM identifies the location and the approximate shape of each cluster. These ones describe the relations among the variables of the data set, and they can be expressed as conditional rules such as "If/Then". The GKPFCM provides membership and typicality values from which a knowledge base is generated through fuzzy rules, which can be used for the classification and characterization of new data.