Probabilistic clustering algorithms for fuzzy rules decomposition

Salgado, Paulo; Igrejas, Getúlio

http://hdl.handle.net/10198/2756

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Authors

Salgado, Paulo

Igrejas, Getúlio

Abstract(s)

The fuzzy c-means (FCM) clustering algorithm is the best known and used method in fuzzy clustering and is generally applied to well defined set of data. In this paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of a flat fuzzy system results a set of decomposed sub-systems that will be conveniently linked into a Parallel Collaborative Structures.

Keywords

Clustering algorithms Fuzzy c-mean Fuzzy systems Relevance

URI

http://hdl.handle.net/10198/2756

Citation

Salgado, Paulo; Igrejas, Getúlio (2007). Probabilistic clustering algorithms for fuzzy rules decomposition. In Workshop on advanced fuzzy and neural control. Valenciennes