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.
CITATION STYLE
Mezei, J., & Sarlin, P. (2016). On a generalized objective function for possibilistic fuzzy clustering. In Communications in Computer and Information Science (Vol. 610, pp. 711–722). Springer Verlag. https://doi.org/10.1007/978-3-319-40596-4_59
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