Abstract
Fuzzy clustering is a useful tool for capturing intrinsic structure of data sets. This paper proposes several formulations for soft transition of fuzzy memberships from probabilistic partition to possibilistic one. In the proposed techniques, the free memberships are given by introducing additional penalty term used in Possibilistic c-Means. The new features of the proposed techniques are demonstrated in several numerical experiments. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Honda, K., Ichihashi, H., Notsu, A., Masulli, F., & Rovetta, S. (2006). Several formulations for graded possibilistic approach to fuzzy clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4259 LNAI, pp. 939–948). Springer Verlag. https://doi.org/10.1007/11908029_97
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