On Tolerant Fuzzy <I>c</I>-Means

• Hamasuna Yukihiro

  Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba

• Endo Yasunori

  Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba

Bibliographic Information

Other Title

• Tolerant Fuzzy <I>c</I>-Means について

Abstract

This paper presents new types of clustering algorithms by using tolerance vector. The tolerance vector is considered from a new viewpoint that the vector shows a correlation between each data and cluster centers in proposed algorithm. First, a new concept of tolerance is introduced into optimization problem. These optimization problems are based on standard fuzzy c-means or entropy regularized fuzzy c-means. Second, the optimization problems with the tolerance are solved by using the Karush-Kuhn-Tucker conditions. Next, new clustering algorithms are constructed based on the unique and explicit optimal solutions of the optimization problems. Finally, the effectiveness of proposed algorithms are verified through some numerical examples.

Journal

• Proceedings of the Fuzzy System Symposium

  Proceedings of the Fuzzy System Symposium 24 (0), 32-32, 2008

  Japan Society for Fuzzy Theory and Intelligent Informatics

Keywords

• Clustering
• Tolerance
• Optimization

Details

• CRID: 1390282680648384256
• NII Article ID: 130005035263
• DOI: 10.14864/fss.24.0.32.0
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed