Fuzzy c-Means Clustering for Data with Tolerance Using Kernel Functions

• Kanzawa Yuchi

  Shibaura Institute of Technology

• Endo Yasunori

  University of Tsukuba

• Miyamoto Sadaaki

  University of Tsukuba

Abstract

A new clustering algorithm is proposed. This algorithm is based on fuzzy c-means for data with tolerance and use a nonlinear transformation from the original pattern space into higher dimensional feature space than the original one with kernel functions. The first, a clustering algorithm for data with tolerance are introduced based on entropy-based method. The second, an objective function in feature space is shown . The third, Karush-Kuhn-Tucker conditions of the objective function is considered. and this condition is re-expressed with kernel function as the representation of an inner product for mapping from original pattern space into higher dimensional feature space than the original one. The last, an iterative algorithms is proposed for the objective function.

Journal

• SCIS & ISIS

  SCIS & ISIS 2006 (0), 1753-1756, 2006

  Japan Society for Fuzzy Theory and Intelligent Informatics

Keywords

• Fuzzy c-means
• Kernel function
• Tolerance

Details

• CRID: 1390282680567178368
• NII Article ID: 130004672426
• DOI: 10.14864/softscis.2006.0.1753.0
• Text Lang: en
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed