An Improved Possibilistic C-Means Algorithm Based on Kernel Methods (original) (raw)

Abstract

A novel fuzzy clustering algorithm, called kernel improved possibilistic c-means (KIPCM) algorithm, is presented based on kernel methods. KIPCM is an extension of the improved possibilistic c-means (IPCM) algorithm. Different from IPCM which is applied in Euclidean space, KIPCM can make data clustering in kernel feature space. With kernel methods the input data can be implicitly mapped into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to calculate in this high-dimensional feature space because we directly calculate inner products from the input data by kernel function. KIPCM can identify clusters of complex shapes and solve nonlinear separable problems better than IPCM and FCM (fuzzy c-means). Our experiments show that the proposed algorithm compares favorably with FCM and IPCM.

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Authors and Affiliations

  1. College of Information Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
    Xiao-Hong Wu & Jian-Jiang Zhou
  2. College of Electrical & Information Engineering, Jiangsu University, Zhenjiang, 212013, China
    Xiao-Hong Wu

Authors

  1. Xiao-Hong Wu
  2. Jian-Jiang Zhou

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Editors and Affiliations

  1. Hong Kong University of Science and Technology,
    Dit-Yan Yeung
  2. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
    James T. Kwok
  3. Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal
    Ana Fred
  4. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy
    Fabio Roli
  5. Faculty of Electrical Engineering, Mathematics and Computer Science, Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
    Dick de Ridder

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© 2006 Springer-Verlag Berlin Heidelberg

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Wu, XH., Zhou, JJ. (2006). An Improved Possibilistic C-Means Algorithm Based on Kernel Methods. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921\_86

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