Linear mixture model approach for selecting fuzzy exponent value in fuzzy c-means algorithm

Francis Okeke, Arnon Karnieli

    Research output: Contribution to journalArticlepeer-review

    38 Scopus citations


    The implementations of both the supervised and unsupervised fuzzy c-means classification algorithms require a priori selection of the fuzzy exponent parameter. This parameter is a weighting exponent and it determines the degree of fuzziness of the membership grades. The determination of an optimal value for this parameter in a fuzzy classification process is problematic and remains an open problem. This paper presents a new and efficient procedure for determining a local optimal value for the fuzzy exponent in the implementation of fuzzy classification technique. Numerical results using simulated image and real data sets are used to illustrate the simplicity and effectiveness of the proposed method.

    Original languageEnglish
    Pages (from-to)117-124
    Number of pages8
    JournalEcological Informatics
    Issue number1
    StatePublished - 1 Jan 2006


    • Fuzzy classification
    • Image processing
    • Linear mixture model

    ASJC Scopus subject areas

    • Ecology, Evolution, Behavior and Systematics
    • Ecology
    • Modeling and Simulation
    • Ecological Modeling
    • Computer Science Applications
    • Computational Theory and Mathematics
    • Applied Mathematics


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