Fuzzy classification function of fuzzy c-means algorithms for data with tolerance

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, two fuzzy classification functions of fuzzy c-means for data with tolerance are proposed. First, two clustering algorithms for data with tolerance are introduced. One is based on the standard method and the other is on the entropy-based one. Second, the fuzzy classification function for fuzzy c-means without tolerance is discussed as the solution of a certain optimization problem. Third, two optimization problems are shown so that the solutions are the fuzzy classification function values for fuzzy c-means algorithms with respect to data with tolerance, respectively. Fourth, Karush-Kuhn-Tucker conditions of two objective functions are considered, and two iterative algorithms are proposed for the optimization problems, respectively. Through some numerical examples, the proposed algorithms are discussed.

Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Pages1081-1088
Number of pages8
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 - Hong Kong
Duration: 2008 Jun 12008 Jun 6

Other

Other2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
CityHong Kong
Period08/6/108/6/6

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Theoretical Computer Science

Cite this

Kanzawa, Y., Endo, Y., & Miyamoto, S. (2008). Fuzzy classification function of fuzzy c-means algorithms for data with tolerance. In IEEE International Conference on Fuzzy Systems (pp. 1081-1088). [4630504] https://doi.org/10.1109/FUZZY.2008.4630504