Please use this identifier to cite or link to this item:
Title: Probabilistic fuzzy clustering algorithm for fuzzy rules decomposition
Authors: Salgado, Paulo
Igrejas, Getúlio
Keywords: Fuzzy clustering
Issue Date: 2007
Citation: Salgado, Paulo; Igrejas, Getúlio (2007) - Probabilistic fuzzy clustering algorithm for fuzzy rules decomposition. In RECPAD - 13º Conferência Portuguesa de Reconhecimento de Padrões. Lisboa
Abstract: 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.
Appears in Collections:DE - Artigos em Proceedings Não Indexados ao ISI/Scopus

Files in This Item:
File Description SizeFormat 
RECPAD2007-1 - Probabilistic fuzzy clustering.pdf41,48 kBAdobe PDFView/Open

FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Degois 

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