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On a Generalized Objective Function for Possibilistic Fuzzy Clustering

József Mezei, Peter Sarlin, On a Generalized Objective Function for Possibilistic Fuzzy Clustering. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems, 610, 711–722, Springer, 2016.

http://dx.doi.org/10.1007/978-3-319-40596-4_59

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.

BibTeX entry:

@INPROCEEDINGS{aconv28502,
  title = {On a Generalized Objective Function for Possibilistic Fuzzy Clustering},
  booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems},
  author = {Mezei, József and Sarlin, Peter},
  volume = {610},
  publisher = {Springer},
  pages = {711–722},
  year = {2016},
  keywords = {Possibilistic clustering;Membership function;Typicality values;Classification},
  ISSN = {1865-0929},
}

Belongs to TUCS Research Unit(s): Institute for Advanced Management Systems Research (IAMSR)

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