1999
DOI: 10.1109/91.771084
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Convex-set-based fuzzy clustering

Abstract: Prototype-based methods are commonly used in cluster analysis and the results may be highly dependent on the prototype used. In this paper, we propose a two-level fuzzy clustering method that involves adaptively expanding and merging convex polytopes, where the convex polytopes are considered as a "flexible" prototype. Therefore, the dependency on the use of a specified prototype can be eliminated. Also, the proposed method makes it possible to effectively represent an arbitrarily distributed data set without … Show more

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Cited by 23 publications
(2 citation statements)
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“…The algorithm for SVM based on weighted feature (WFSVM) [5] is the same as Traditional SVM but the diagonal of the kernel matrix is replaced with the weights of the features.…”
Section: Training Phasementioning
confidence: 99%
See 1 more Smart Citation
“…The algorithm for SVM based on weighted feature (WFSVM) [5] is the same as Traditional SVM but the diagonal of the kernel matrix is replaced with the weights of the features.…”
Section: Training Phasementioning
confidence: 99%
“…PC, CE and PE validity measures are lacking direct connection to geometrical property. But S validity function includes geometrical properties [4] [5] and it is a proportion of compactness to separation. Here S measure is used to validate the clustering.…”
Section: Introductionmentioning
confidence: 99%