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A modification to improve possibilistic fuzzy cluster analysis

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2 Author(s)
Timm, H. ; Dept. of Knowledge Process. & Language Eng., Otto-von-Guericke-Univ., Magdeburg, Germany ; Kruse, R.

We explore an approach to possibilistic fuzzy clustering that avoids a severe drawback of the conventional approach, namely that the objective function is truly minimized only if all cluster centers are identical. Our approach is based on the idea that this undesired property can be avoided if we introduce a mutual repulsion of the clusters, so that they are forced away from each other. We develop this approach for the possibilistic fuzzy c-means algorithm and the Gustafson-Kessel algorithm

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Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on  (Volume:2 )

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