Clustering algorithms for fuzzy rules decomposition

Bibliographic Details
Main Author: Salgado, Paulo
Publication Date: 2007
Other Authors: Igrejas, Getúlio
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10198/2757
Summary: This paper presents the development, testing and evaluation of generalized Possibilistic fuzzy c-means (FCM) algorithms applied to fuzzy sets. Clustering is formulated as a constrained minimization problem, whose solution depends on the constraints imposed on the membership function of the cluster and on the relevance measure of the fuzzy rules. This fuzzy clustering of fuzzy rules 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 Hierarchical Prioritized Structures.
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spelling Clustering algorithms for fuzzy rules decompositionFuzzy clusteringRules decompositionThis paper presents the development, testing and evaluation of generalized Possibilistic fuzzy c-means (FCM) algorithms applied to fuzzy sets. Clustering is formulated as a constrained minimization problem, whose solution depends on the constraints imposed on the membership function of the cluster and on the relevance measure of the fuzzy rules. This fuzzy clustering of fuzzy rules 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 Hierarchical Prioritized Structures.Biblioteca Digital do IPBSalgado, PauloIgrejas, Getúlio2010-11-09T15:54:13Z20072007-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10198/2757engSalgado, Paulo; Igrejas, Getúlio (2007). Clustering algorithms for fuzzy rules decomposition. In Proceedings of the UK Computational Intelligence Workshop. Londoninfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-25T11:55:15Zoai:bibliotecadigital.ipb.pt:10198/2757Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T11:16:47.487989Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Clustering algorithms for fuzzy rules decomposition
title Clustering algorithms for fuzzy rules decomposition
spellingShingle Clustering algorithms for fuzzy rules decomposition
Salgado, Paulo
Fuzzy clustering
Rules decomposition
title_short Clustering algorithms for fuzzy rules decomposition
title_full Clustering algorithms for fuzzy rules decomposition
title_fullStr Clustering algorithms for fuzzy rules decomposition
title_full_unstemmed Clustering algorithms for fuzzy rules decomposition
title_sort Clustering algorithms for fuzzy rules decomposition
author Salgado, Paulo
author_facet Salgado, Paulo
Igrejas, Getúlio
author_role author
author2 Igrejas, Getúlio
author2_role author
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.contributor.author.fl_str_mv Salgado, Paulo
Igrejas, Getúlio
dc.subject.por.fl_str_mv Fuzzy clustering
Rules decomposition
topic Fuzzy clustering
Rules decomposition
description This paper presents the development, testing and evaluation of generalized Possibilistic fuzzy c-means (FCM) algorithms applied to fuzzy sets. Clustering is formulated as a constrained minimization problem, whose solution depends on the constraints imposed on the membership function of the cluster and on the relevance measure of the fuzzy rules. This fuzzy clustering of fuzzy rules 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 Hierarchical Prioritized Structures.
publishDate 2007
dc.date.none.fl_str_mv 2007
2007-01-01T00:00:00Z
2010-11-09T15:54:13Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10198/2757
url http://hdl.handle.net/10198/2757
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Salgado, Paulo; Igrejas, Getúlio (2007). Clustering algorithms for fuzzy rules decomposition. In Proceedings of the UK Computational Intelligence Workshop. London
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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