On a Generalized Objective Function for Possibilistic Fuzzy Clustering

A4 Konferenspublikationer


Interna författare/redaktörer


Publikationens författare: Mezei J, Sarlin P
Redaktörer: Carvalho JP, Lesot M-J, Kaymak U, Vieira S, Bouchon-Meunier B, Yager RR
Förläggare: SPRINGER INT PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Publiceringsår: 2016
Förläggare: Springer
Moderpublikationens namn: Information Processing and Management of Uncertainty in Knowledge-Based Systems
Tidskriftsakronym: COMM COM INF SC
Volym: 610
Artikelns första sida, sidnummer: 711
Artikelns sista sida, sidnummer: 722
Antal sidor: 12
ISBN: 978-3-319-40595-7
eISBN: 978-3-319-40596-4
ISSN: 1865-0929


Abstrakt

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.


Nyckelord

Classification, Membership function, Possibilistic clustering, Typicality values

Senast uppdaterad 2020-20-02 vid 07:43