Implementing bootstrap in ward´s algorithm to estimate the number of clusters
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Sistemas & Gestão |
Texto Completo: | https://www.revistasg.uff.br/sg/article/view/V4N2A1 |
Resumo: | In this paper we show how bootstrap can be implemented in hierarchical clustering algorithms as a strategy to estimate the number of clusters (k). Ward´s algorithm was chosen as an example. The estimation of k is based on a similarity coefficient and three statistical stopping rules, pseudo F, pseudo T2 and CCC. The performance of the estimation procedure was evaluated through Monte Carlo simulation considering data consisting of correlated and uncorrelated variables, nonoverlapping and overlapping clusters. The estimation procedure discussed in this paper can be used with clustering algorithms other than Ward´s and also to provide initial solutions for non-hierarchical grouping methods. |
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Sistemas & Gestão |
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Implementing bootstrap in ward´s algorithm to estimate the number of clustersWard´s algorithmEstimation of number of clustersBootstrap In this paper we show how bootstrap can be implemented in hierarchical clustering algorithms as a strategy to estimate the number of clusters (k). Ward´s algorithm was chosen as an example. The estimation of k is based on a similarity coefficient and three statistical stopping rules, pseudo F, pseudo T2 and CCC. The performance of the estimation procedure was evaluated through Monte Carlo simulation considering data consisting of correlated and uncorrelated variables, nonoverlapping and overlapping clusters. The estimation procedure discussed in this paper can be used with clustering algorithms other than Ward´s and also to provide initial solutions for non-hierarchical grouping methods.ABEC2009-09-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistasg.uff.br/sg/article/view/V4N2A110.7177/sg.2009.V4N2A1Sistemas & Gestão; v. 4 n. 2 (2009): Agosto/2009; 89-1071980-516010.7177/sg.2009.v4.n2reponame:Sistemas & Gestãoinstname:Universidade Federal Fluminense (UFF)instacron:UFFporhttps://www.revistasg.uff.br/sg/article/view/V4N2A1/V4N2A1Copyright (c) 2015 Sistemas & Gestãoinfo:eu-repo/semantics/openAccessMingoti, Sueli A.Felix, Francisco N.2023-01-09T18:18:55Zoai:ojs.www.revistasg.uff.br:article/65Revistahttps://www.revistasg.uff.br/sgPUBhttps://www.revistasg.uff.br/sg/oai||sg.revista@gmail.com|| periodicos@proppi.uff.br1980-51601980-5160opendoar:2023-01-09T18:18:55Sistemas & Gestão - Universidade Federal Fluminense (UFF)false |
dc.title.none.fl_str_mv |
Implementing bootstrap in ward´s algorithm to estimate the number of clusters |
title |
Implementing bootstrap in ward´s algorithm to estimate the number of clusters |
spellingShingle |
Implementing bootstrap in ward´s algorithm to estimate the number of clusters Mingoti, Sueli A. Ward´s algorithm Estimation of number of clusters Bootstrap |
title_short |
Implementing bootstrap in ward´s algorithm to estimate the number of clusters |
title_full |
Implementing bootstrap in ward´s algorithm to estimate the number of clusters |
title_fullStr |
Implementing bootstrap in ward´s algorithm to estimate the number of clusters |
title_full_unstemmed |
Implementing bootstrap in ward´s algorithm to estimate the number of clusters |
title_sort |
Implementing bootstrap in ward´s algorithm to estimate the number of clusters |
author |
Mingoti, Sueli A. |
author_facet |
Mingoti, Sueli A. Felix, Francisco N. |
author_role |
author |
author2 |
Felix, Francisco N. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Mingoti, Sueli A. Felix, Francisco N. |
dc.subject.por.fl_str_mv |
Ward´s algorithm Estimation of number of clusters Bootstrap |
topic |
Ward´s algorithm Estimation of number of clusters Bootstrap |
description |
In this paper we show how bootstrap can be implemented in hierarchical clustering algorithms as a strategy to estimate the number of clusters (k). Ward´s algorithm was chosen as an example. The estimation of k is based on a similarity coefficient and three statistical stopping rules, pseudo F, pseudo T2 and CCC. The performance of the estimation procedure was evaluated through Monte Carlo simulation considering data consisting of correlated and uncorrelated variables, nonoverlapping and overlapping clusters. The estimation procedure discussed in this paper can be used with clustering algorithms other than Ward´s and also to provide initial solutions for non-hierarchical grouping methods. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-09-23 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.revistasg.uff.br/sg/article/view/V4N2A1 10.7177/sg.2009.V4N2A1 |
url |
https://www.revistasg.uff.br/sg/article/view/V4N2A1 |
identifier_str_mv |
10.7177/sg.2009.V4N2A1 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://www.revistasg.uff.br/sg/article/view/V4N2A1/V4N2A1 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2015 Sistemas & Gestão info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2015 Sistemas & Gestão |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
ABEC |
publisher.none.fl_str_mv |
ABEC |
dc.source.none.fl_str_mv |
Sistemas & Gestão; v. 4 n. 2 (2009): Agosto/2009; 89-107 1980-5160 10.7177/sg.2009.v4.n2 reponame:Sistemas & Gestão instname:Universidade Federal Fluminense (UFF) instacron:UFF |
instname_str |
Universidade Federal Fluminense (UFF) |
instacron_str |
UFF |
institution |
UFF |
reponame_str |
Sistemas & Gestão |
collection |
Sistemas & Gestão |
repository.name.fl_str_mv |
Sistemas & Gestão - Universidade Federal Fluminense (UFF) |
repository.mail.fl_str_mv |
||sg.revista@gmail.com|| periodicos@proppi.uff.br |
_version_ |
1798320142347665408 |