A new kernel-based fuzzy clustering approach: Support vector clustering with cell growing

Jung Hsien Chiang, Pei Yi Hao

Research output: Contribution to journalArticle

188 Citations (Scopus)

Abstract

In this paper, the support vector clustering is extended to an adaptive cell growing model which maps data points to a high dimensional feature space through a desired kernel function. This generalized model is called multiple spheres support vector clustering, which essentially identities dense regions in the original space by finding their corresponding spheres with minimal radius in the feature space. A multisphere clustering algorithm based on adaptive cluster cell growing method is developed, whereby it is possible to obtain the grade of memberships, as well as cluster prototypes in partition. The effectiveness of the proposed algorithm is demonstrated for the problem of arbitrary cluster shapes and for prototype identification in an actual application to a handwritten digit data set.

Original languageEnglish
Pages (from-to)518-527
Number of pages10
JournalIEEE Transactions on Fuzzy Systems
Volume11
Issue number4
DOIs
Publication statusPublished - 2003 Aug 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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