Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms

Detalhes bibliográficos
Autor(a) principal: Adelaide Figueiredo
Data de Publicação: 2015
Outros Autores: Gomes,P
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/7134
http://dx.doi.org/10.1080/03610918.2014.901353
Resumo: We consider n individuals described by p variables, represented by points of the surface of unit hypersphere. We suppose that the individuals are fixed and the set of variables comes from a mixture of bipolar Watson distributions. For the mixture identification, we use EM and dynamic clusters algorithms, which enable us to obtain a partition of the set of variables into clusters of variables.Our aim is to evaluate the clusters obtained in these algorithms, using measures of within-groups variability and between-groups variability and compare these clusters with those obtained in other clustering approaches, by analyzing simulated and real data.
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spelling Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of AlgorithmsWe consider n individuals described by p variables, represented by points of the surface of unit hypersphere. We suppose that the individuals are fixed and the set of variables comes from a mixture of bipolar Watson distributions. For the mixture identification, we use EM and dynamic clusters algorithms, which enable us to obtain a partition of the set of variables into clusters of variables.Our aim is to evaluate the clusters obtained in these algorithms, using measures of within-groups variability and between-groups variability and compare these clusters with those obtained in other clustering approaches, by analyzing simulated and real data.2018-01-19T18:01:34Z2015-01-01T00:00:00Z2015info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/7134http://dx.doi.org/10.1080/03610918.2014.901353engAdelaide FigueiredoGomes,Pinfo: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:RCAAP2023-05-15T10:19:54Zoai:repositorio.inesctec.pt:123456789/7134Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:24.908509Repositório 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 Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms
title Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms
spellingShingle Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms
Adelaide Figueiredo
title_short Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms
title_full Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms
title_fullStr Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms
title_full_unstemmed Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms
title_sort Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms
author Adelaide Figueiredo
author_facet Adelaide Figueiredo
Gomes,P
author_role author
author2 Gomes,P
author2_role author
dc.contributor.author.fl_str_mv Adelaide Figueiredo
Gomes,P
description We consider n individuals described by p variables, represented by points of the surface of unit hypersphere. We suppose that the individuals are fixed and the set of variables comes from a mixture of bipolar Watson distributions. For the mixture identification, we use EM and dynamic clusters algorithms, which enable us to obtain a partition of the set of variables into clusters of variables.Our aim is to evaluate the clusters obtained in these algorithms, using measures of within-groups variability and between-groups variability and compare these clusters with those obtained in other clustering approaches, by analyzing simulated and real data.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01T00:00:00Z
2015
2018-01-19T18:01:34Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/7134
http://dx.doi.org/10.1080/03610918.2014.901353
url http://repositorio.inesctec.pt/handle/123456789/7134
http://dx.doi.org/10.1080/03610918.2014.901353
dc.language.iso.fl_str_mv eng
language eng
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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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
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