A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering

Detalhes bibliográficos
Autor(a) principal: Suleman, A.
Data de Publicação: 2015
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/10583
Resumo: We propose an alternative approach to fuzzy c-means clustering which eliminates the weighting exponent parameter of conventional algorithms. It is based on a particular convex factorisation of data matrix. The proposed method is invariant under certain linear transformations of the data including principal component analysis. We tested its accuracy using both synthetic data and real datasets, and compared it to that provided by the usual fuzzy c-means algorithm. We were able to ascertain that our proposal can be a credible yet easier alternative to this approach to fuzzy clustering. Moreover, it showed no noticeable sensitivity to the initial guess of the partition matrix.
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spelling A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clusteringFuzzy clusteringFuzzy c-meansSemi-nonnegative matrix factorisationPrincipal component analysisWe propose an alternative approach to fuzzy c-means clustering which eliminates the weighting exponent parameter of conventional algorithms. It is based on a particular convex factorisation of data matrix. The proposed method is invariant under certain linear transformations of the data including principal component analysis. We tested its accuracy using both synthetic data and real datasets, and compared it to that provided by the usual fuzzy c-means algorithm. We were able to ascertain that our proposal can be a credible yet easier alternative to this approach to fuzzy clustering. Moreover, it showed no noticeable sensitivity to the initial guess of the partition matrix.Elsevier2016-01-07T18:14:59Z2015-01-01T00:00:00Z20152019-05-09T13:27:47Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/10583eng0165-011410.1016/j.fss.2014.07.021Suleman, A.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-25T17:44:13ZPortal AgregadorONG
dc.title.none.fl_str_mv A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering
title A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering
spellingShingle A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering
Suleman, A.
Fuzzy clustering
Fuzzy c-means
Semi-nonnegative matrix factorisation
Principal component analysis
title_short A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering
title_full A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering
title_fullStr A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering
title_full_unstemmed A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering
title_sort A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering
author Suleman, A.
author_facet Suleman, A.
author_role author
dc.contributor.author.fl_str_mv Suleman, A.
dc.subject.por.fl_str_mv Fuzzy clustering
Fuzzy c-means
Semi-nonnegative matrix factorisation
Principal component analysis
topic Fuzzy clustering
Fuzzy c-means
Semi-nonnegative matrix factorisation
Principal component analysis
description We propose an alternative approach to fuzzy c-means clustering which eliminates the weighting exponent parameter of conventional algorithms. It is based on a particular convex factorisation of data matrix. The proposed method is invariant under certain linear transformations of the data including principal component analysis. We tested its accuracy using both synthetic data and real datasets, and compared it to that provided by the usual fuzzy c-means algorithm. We were able to ascertain that our proposal can be a credible yet easier alternative to this approach to fuzzy clustering. Moreover, it showed no noticeable sensitivity to the initial guess of the partition matrix.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01T00:00:00Z
2015
2016-01-07T18:14:59Z
2019-05-09T13:27:47Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10071/10583
url http://hdl.handle.net/10071/10583
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0165-0114
10.1016/j.fss.2014.07.021
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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