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.


Possibilistic clustering Membership function Typicality values Classification 


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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Social Sciences, Business and EconomicsÅbo Akademi UniversityTurkuFinland
  2. 2.Department of EconomicsHanken School of EconomicsHelsinkiFinland
  3. 3.RiskLab Finland at Arcada University of Applied SciencesHelsinkiFinland

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