Robust kernel fuzzy clustering

Weiwei Du, Kohei Inoue, Kiichi Urahama

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


We present a method for extracting arbitrarily shaped clusters buried in uniform noise data. The popular k-means algorithm is firstly fuzzified with addition of entropic terms to the objective function of data partitioning problem. This fuzzy clustering is then kernelized for adapting to the arbitrary shape of clusters. Finally, the Euclidean distance in this kernelized fuzzy clustering is modified to a robust one for avoiding the influence of noisy background data. This robust kernel fuzzy clustering method is shown to outperform every its predecessor: fuzzified k-means, robust fuzzified k-means and kernel fuzzified k-means algorithms.

Original languageEnglish
Pages (from-to)454-461
Number of pages8
JournalUnknown Journal
Issue numberPART I
Publication statusPublished - 2005

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture


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