A study on parallel versus sequential relational fuzzy clustering methods

Detalhes bibliográficos
Autor(a) principal: Felizardo, Rui Miguel Meireles
Data de Publicação: 2011
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/5663
Resumo: Dissertação para obtenção do Grau de Mestre em Engenharia Informática
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spelling A study on parallel versus sequential relational fuzzy clustering methodsRelational dataRelational fuzzy clusteringFuzzy additive spectral clusteringNumber of clustersValidation indicesDissertação para obtenção do Grau de Mestre em Engenharia InformáticaRelational Fuzzy Clustering is a recent growing area of study. New algorithms have been developed,as FastMap Fuzzy c-Means (FMFCM) and the Fuzzy Additive Spectral Clustering Method(FADDIS), for which it had been obtained interesting experimental results in the corresponding founding works. Since these algorithms are new in the context of the Fuzzy Relational clustering community, not many experimental studies are available. This thesis comes in response to the need of further investigation on these algorithms, concerning a comparative experimental study from the two families of algorithms: the parallel and the sequential versions. These two families of algorithms differ in the way they cluster data. Parallel versions extract clusters simultaneously from data and need the number of clusters as an input parameter of the algorithms, while the sequential versions extract clusters one-by-one until a stop condition is verified, being the number of clusters a natural output of the algorithm. The algorithms are studied in their effectiveness on retrieving good cluster structures by analysing the quality of the partitions as well as the determination of the number of clusters by applying several validation measures. An extensive simulation study has been conducted over two data generators specifically constructed for the algorithms under study, in particular to study their robustness for data with noise. Results with benchmark real data are also discussed. Particular attention is made on the most adequate pre-processing on relational data, in particular on the pseudo-inverse Laplacian transformation.Faculdade de Ciências e TecnologiaNascimento, SusanaRUNFelizardo, Rui Miguel Meireles2011-05-25T11:27:22Z20112011-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/5663enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T17:09:00Zoai:run.unl.pt:10362/5663Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:09Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A study on parallel versus sequential relational fuzzy clustering methods
title A study on parallel versus sequential relational fuzzy clustering methods
spellingShingle A study on parallel versus sequential relational fuzzy clustering methods
Felizardo, Rui Miguel Meireles
Relational data
Relational fuzzy clustering
Fuzzy additive spectral clustering
Number of clusters
Validation indices
title_short A study on parallel versus sequential relational fuzzy clustering methods
title_full A study on parallel versus sequential relational fuzzy clustering methods
title_fullStr A study on parallel versus sequential relational fuzzy clustering methods
title_full_unstemmed A study on parallel versus sequential relational fuzzy clustering methods
title_sort A study on parallel versus sequential relational fuzzy clustering methods
author Felizardo, Rui Miguel Meireles
author_facet Felizardo, Rui Miguel Meireles
author_role author
dc.contributor.none.fl_str_mv Nascimento, Susana
RUN
dc.contributor.author.fl_str_mv Felizardo, Rui Miguel Meireles
dc.subject.por.fl_str_mv Relational data
Relational fuzzy clustering
Fuzzy additive spectral clustering
Number of clusters
Validation indices
topic Relational data
Relational fuzzy clustering
Fuzzy additive spectral clustering
Number of clusters
Validation indices
description Dissertação para obtenção do Grau de Mestre em Engenharia Informática
publishDate 2011
dc.date.none.fl_str_mv 2011-05-25T11:27:22Z
2011
2011-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/5663
url http://hdl.handle.net/10362/5663
dc.language.iso.fl_str_mv eng
language eng
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.publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
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
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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