On a Generalized Objective Function for Possibilistic Fuzzy Clustering

Jozsef Mezei, Peter Sarlin

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 tammikuuta 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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