On a Generalized Objective Function for Possibilistic Fuzzy Clustering

    Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

    Abstrakti

    Possibilistic clustering methods have gained attention in both applied and theoretical research. In this paper, we formulate a general objective function for possibilistic clustering. The objective function can be used as the basis of a mixed clustering approach incorporating both fuzzy memberships and possibilistic typicality values to overcome various problems of previous clustering approaches. We use numerical experiments for a classification task to illustrate the usefulness of the proposal. Beyond a performance comparison with the three most widely used (mixed) possibilistic clustering methods, this also outlines the use of possibilistic clustering for descriptive classification via memberships to a variety of different class clusters. We find that possibilistic clustering using the general objective function outperforms traditional approaches in terms of various performance measures.
    AlkuperäiskieliEi tiedossa
    OtsikkoInformation Processing and Management of Uncertainty in Knowledge-Based Systems
    ToimittajatJP Carvalho, Lesot M-J, U Kaymak, S Vieira, B Bouchon-Meunier, RR Yager
    KustantajaSpringer
    Sivut711–722
    Sivumäärä12
    ISBN (elektroninen)978-3-319-40596-4
    ISBN (painettu)978-3-319-40595-7
    DOI - pysyväislinkit
    TilaJulkaistu - 2016
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
    TapahtumaInternational conference on information processing and management of uncertainty in knowledge-based systems - IPMU 2016
    Kesto: 1 tammik. 2016 → …

    Konferenssi

    KonferenssiInternational conference on information processing and management of uncertainty in knowledge-based systems
    Ajanjakso01/01/16 → …

    Keywords

    • Classification
    • Membership function
    • Possibilistic clustering
    • Typicality values

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