Differential evolution fuzzy clustering algorithm based on kernel methods

  • Authors:

  • Libiao Zhang;Ming Ma;Xiaohua Liu;Caitang Sun;Miao Liu;Chunguang Zhou

  • Affiliations:

  • College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China, Changchun, P.R. China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China, Changchun, P.R. China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China, Changchun, P.R. China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China, Changchun, P.R. China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China, Changchun, P.R. China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China, Changchun, P.R. China

  • Venue:
  • RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
  • Year:
  • 2006

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Abstract

A new fuzzy clustering algorithm is proposed. By using kernel methods, this paper maps the data in the original space into a high-dimensional feature space in which a fuzzy dissimilarity matrix is constructed. It not only accurately reflects the difference of attributes among classes, but also maps the difference among samples in the high-dimensional feature space into the two-dimensional plane. Using the particularity of strong global search ability and quickly converging speed of Differential Evolution (DE) algorithms, it optimizes the coordinates of the samples distributed randomly on a plane. The clustering for random distributing shapes of samples is realized. It not only overcomes the dependence of clustering validity on the space distribution of samples, but also improves the flexibility of the clustering and the visualization of high-dimensional samples. Numerical experiments show the effectiveness of the proposed algorithm