Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering

Detalhes bibliográficos
Autor(a) principal: Almeida, J. A. S.
Data de Publicação: 2007
Outros Autores: Barbosa, L. M. S., Pais, A. A. C. C., Formosinho, S. J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/5042
Resumo: Techniques based on agglomerative hierarchical clustering constitute one of the most frequent approaches in unsupervised clustering. Some are based on the single linkage methodology, which has been shown to produce good results with sets of clusters of various sizes and shapes. However, the application of this type of algorithms in a wide variety of fields has posed a number of problems, such as the sensitivity to outliers and fluctuations in the density of data points. Additionally, these algorithms do not usually allow for automatic clustering.
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spelling Improving hierarchical cluster analysis: A new method with outlier detection and automatic clusteringClusteringUnsupervised pattern recognitionHierarchical cluster analysisSingle linkageOutlier removalTechniques based on agglomerative hierarchical clustering constitute one of the most frequent approaches in unsupervised clustering. Some are based on the single linkage methodology, which has been shown to produce good results with sets of clusters of various sizes and shapes. However, the application of this type of algorithms in a wide variety of fields has posed a number of problems, such as the sensitivity to outliers and fluctuations in the density of data points. Additionally, these algorithms do not usually allow for automatic clustering.http://www.sciencedirect.com/science/article/B6TFP-4MYVG3H-1/1/714f82559ca43bbdbff8ad8a1e2d14ac2007info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleaplication/PDFhttp://hdl.handle.net/10316/5042http://hdl.handle.net/10316/5042engChemometrics and Intelligent Laboratory Systems. 87:2 (2007) 208-217Almeida, J. A. S.Barbosa, L. M. S.Pais, A. A. C. C.Formosinho, S. J.info: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:RCAAP2020-05-25T13:12:42Zoai:estudogeral.uc.pt:10316/5042Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:01:09.843094Repositório 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 Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering
title Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering
spellingShingle Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering
Almeida, J. A. S.
Clustering
Unsupervised pattern recognition
Hierarchical cluster analysis
Single linkage
Outlier removal
title_short Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering
title_full Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering
title_fullStr Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering
title_full_unstemmed Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering
title_sort Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering
author Almeida, J. A. S.
author_facet Almeida, J. A. S.
Barbosa, L. M. S.
Pais, A. A. C. C.
Formosinho, S. J.
author_role author
author2 Barbosa, L. M. S.
Pais, A. A. C. C.
Formosinho, S. J.
author2_role author
author
author
dc.contributor.author.fl_str_mv Almeida, J. A. S.
Barbosa, L. M. S.
Pais, A. A. C. C.
Formosinho, S. J.
dc.subject.por.fl_str_mv Clustering
Unsupervised pattern recognition
Hierarchical cluster analysis
Single linkage
Outlier removal
topic Clustering
Unsupervised pattern recognition
Hierarchical cluster analysis
Single linkage
Outlier removal
description Techniques based on agglomerative hierarchical clustering constitute one of the most frequent approaches in unsupervised clustering. Some are based on the single linkage methodology, which has been shown to produce good results with sets of clusters of various sizes and shapes. However, the application of this type of algorithms in a wide variety of fields has posed a number of problems, such as the sensitivity to outliers and fluctuations in the density of data points. Additionally, these algorithms do not usually allow for automatic clustering.
publishDate 2007
dc.date.none.fl_str_mv 2007
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/10316/5042
http://hdl.handle.net/10316/5042
url http://hdl.handle.net/10316/5042
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Chemometrics and Intelligent Laboratory Systems. 87:2 (2007) 208-217
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv aplication/PDF
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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