Please use this identifier to cite or link to this item:
Title: Probabilistic clustering algorithms for fuzzy rules decomposition
Author: Salgado, Paulo
Igrejas, Getúlio
Keywords: Clustering algorithms
Fuzzy c-mean
Fuzzy systems
Issue Date: 2007
Citation: Salgado, Paulo; Igrejas, Getúlio (2007) - Probabilistic clustering algorithms for fuzzy rules decomposition. In Workshop on advanced fuzzy and neural control. Valenciennes
Abstract: 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.
Appears in Collections:DE - Artigos em Proceedings Não Indexados ao ISI/Scopus

Files in This Item:
File Description SizeFormat 
IFAC-AFNC07 - Probabilistic clustering.pdf511,47 kBAdobe PDFView/Open

FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Degois 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.