Robust kernel fuzzy clustering

Weiwei Du, Kohei Inoue, Kiichi Urahama

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)

    Abstract

    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
    Volume3613
    Issue numberPART I
    Publication statusPublished - 2005

    All Science Journal Classification (ASJC) codes

    • Hardware and Architecture

    Fingerprint

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

    Cite this