Robust kernel fuzzy clustering

Weiwei Du, Kohei Inoue, Kiichi Urahama

    Research output: Contribution to journalConference articlepeer-review

    13 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
    JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
    Issue numberPART I
    Publication statusPublished - 2005
    EventSecond International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China
    Duration: Aug 27 2005Aug 29 2005

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • General Computer Science


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