On fuzzy c-means for data with tolerance

  • Authors:

  • Ryuichi Murata;Yasunori Endo;Hideyuki Haruyama;Sadaaki Miyamoto

  • Affiliations:

  • Graduate School of Systems and Information Engineering, University of Tsukuba Ibaraki, Japan;Department of Risk Engineering Faculty of Systems and Information Engineering, University of Tsukuba Ibaraki, Japan;Graduate School of Systems and Information Engineering, University of Tsukuba Ibaraki, Japan;Department of Risk Engineering Faculty of Systems and Information Engineering, University of Tsukuba Ibaraki, Japan

  • Venue:
  • MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
  • Year:
  • 2006

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Abstract

This paper presents two new clustering algorithms which are based on the entropy regularized fuzzy c-means and can treat data with some errors. First, the tolerance which means the permissible range of the error is introduced into optimization problems which relate with clustering, and the tolerance is formulated. Next, the problems are solved using Kuhn-Tucker conditions. Last, the algorithms are constructed based on the results of solving the problems.