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Please use this identifier to cite or link to this item: http://hdl.handle.net/10198/2756

Título: Probabilistic clustering algorithms for fuzzy rules decomposition
Autor: Salgado, Paulo
Igrejas, Getúlio
Palavras-chave: Clustering algorithms
Fuzzy c-mean
Fuzzy system
relevance
Issue Date: 2007
Citação: Salgado, Paulo; Igrejas, Getúlio (2007) - Probabilistic clustering algorithms for fuzzy rules decomposition. In Workshop on advanced fuzzy and neural control. Valenciennes
Resumo: 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.
URI: http://hdl.handle.net/10198/2756
Appears in Collections:DE - Artigos em Proceedings Não Indexados ao ISI

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