Robust kernel fuzzy clustering

Weiwei Du, Kohei Inoue, Kiichi Urahama

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (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 entropie 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
    Title of host publicationFuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings
    PublisherSpringer Verlag
    Number of pages8
    ISBN (Print)9783540283126
    Publication statusPublished - 2006
    Event2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsa, China
    Duration: Aug 27 2005Aug 29 2005

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3613 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Other2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • General Computer Science


    Dive into the research topics of 'Robust kernel fuzzy clustering'. Together they form a unique fingerprint.

    Cite this