On a Generalized Objective Function for Possibilistic Fuzzy Clustering

Jozsef Mezei, Peter Sarlin

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

Abstract

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.
Original languageUndefined/Unknown
Title of host publicationInformation Processing and Management of Uncertainty in Knowledge-Based Systems
EditorsJP Carvalho, Lesot M-J, U Kaymak, S Vieira, B Bouchon-Meunier, RR Yager
PublisherSpringer
Pages711–722
Number of pages12
ISBN (Electronic)978-3-319-40596-4
ISBN (Print)978-3-319-40595-7
DOIs
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventInternational conference on information processing and management of uncertainty in knowledge-based systems - IPMU 2016
Duration: 1 Jan 2016 → …

Conference

ConferenceInternational conference on information processing and management of uncertainty in knowledge-based systems
Period01/01/16 → …

Keywords

  • Classification
  • Membership function
  • Possibilistic clustering
  • Typicality values

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