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 language | Undefined/Unknown |
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Title of host publication | Information Processing and Management of Uncertainty in Knowledge-Based Systems |
Editors | JP Carvalho, Lesot M-J, U Kaymak, S Vieira, B Bouchon-Meunier, RR Yager |
Publisher | Springer |
Pages | 711–722 |
Number of pages | 12 |
ISBN (Electronic) | 978-3-319-40596-4 |
ISBN (Print) | 978-3-319-40595-7 |
DOIs | |
Publication status | Published - 2016 |
MoE publication type | A4 Article in a conference publication |
Event | International conference on information processing and management of uncertainty in knowledge-based systems - IPMU 2016 Duration: 1 Jan 2016 → … |
Conference
Conference | International conference on information processing and management of uncertainty in knowledge-based systems |
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Period | 01/01/16 → … |
Keywords
- Classification
- Membership function
- Possibilistic clustering
- Typicality values