Author(s):
Salgado, Paulo 
; Igrejas, Getúlio
Date: 2007
Persistent ID: http://hdl.handle.net/10198/2774
Origin: Biblioteca Digital do IPB
Subject(s): Fuzzy clustering
Document details
Probabilistic fuzzy clustering algorithm for fuzzy rules decomposition
Description
The Fuzzy C-Means (FCM) clustering algorithm is the best known and the most used method for fuzzy clustering and is generally applied to well defined sets of data. In this work a generalized Probabilistic Fuzzy C-Means (PFCM) algorithm is proposed and applied to fuzzy sets clustering. The methodology presented 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 the result is a set of decomposed sub-systems that will be conveniently linked into a Parallel Collaborative Structure.
The Fuzzy C-Means (FCM) clustering algorithm is the best known and the most used method for fuzzy clustering and is generally applied to well defined sets of data. In this work a generalized Probabilistic Fuzzy C-Means (PFCM) algorithm is proposed and applied to fuzzy sets clustering. The methodology presented 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 the result is a set of decomposed sub-systems that will be conveniently linked into a Parallel Collaborative Structure.
Document Type
Conference Object
Language English
Language English
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