On a Generalized Objective Function for Possibilistic Fuzzy Clustering

A4 Conference proceedings


Internal Authors/Editors


Publication Details

List of Authors: Mezei J, Sarlin P
Editors: Carvalho JP, Lesot M-J, Kaymak U, Vieira S, Bouchon-Meunier B, Yager RR
Publisher: SPRINGER INT PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Publication year: 2016
Publisher: Springer
Book title: Information Processing and Management of Uncertainty in Knowledge-Based Systems
Journal acronym: COMM COM INF SC
Volume number: 610
Start page: 711
End page: 722
Number of pages: 12
ISBN: 978-3-319-40595-7
eISBN: 978-3-319-40596-4
ISSN: 1865-0929


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.


Keywords

Classification, Membership function, Possibilistic clustering, Typicality values

Last updated on 2019-12-11 at 03:03