Fuzzy clustering for documents based on optimization of classifier using the genetic algorithm

  • Authors:

  • Ju-In Youn;He-Jue Eun;Yong-Sung Kim

  • Affiliations:

  • Division of Electronics and Information Engineering, Chonbuk National University, Jeonju, Republic of Korea;Division of Electronics and Information Engineering, Chonbuk National University, Jeonju, Republic of Korea;Division of Electronics and Information Engineering, Chonbuk National University, Jeonju, Republic of Korea

  • Venue:
  • ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
  • Year:
  • 2005

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Abstract

It is a problem that established document categorization method reflects the semantic relation inaccurately at feature expression of document. For the purpose of solving this problem, we propose a genetic algorithm and C-Means clustering algorithm for choosing an appropriate set of fuzzy clustering for classification problems of documents. The aim of the proposed method is to find a minimum set of fuzzy cluster that can correctly classify all training documents. The number of fuzzy pseudo-partition and the shapes of the fuzzy membership functions that we use the classification criteria are determined by the genetic algorithms. Then, the classifier decides using fuzzy c-means clustering algorithms for documents classification. A solution obtained by the genetic algorithm is a set of fuzzy clustering, and its fitness function is determined by fuzzy membership function.