PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Sistemas & Gestão |
Texto Completo: | https://www.revistasg.uff.br/sg/article/view/V7N1A6 |
Resumo: | The investigation of similarity patterns between companies can be accomplished by the formation of similarity groups. However, there is always doubt about the best way to build clusters. Thus, the objective of this work was to study some clustering methods and some possible group sizes, so as to obtain a good clustering. In addition, an index for evaluating the clusters formed was proposed. Six clustering methods and three group sizes were considered. After the application of cluster analysis, the groups formed were assessed by the discriminant analysis, the Kappa coefficient and an index developed to assess the initial clusters. Overall, it was found that the best option would be to apply the MW method with three groups and after, to apply the discriminant analysis to obtain an appropriate final number of companies per group. This procedure allowed obtaining groups relatively similar in terms of number of elements per group. Furthermore, an interesting alternative is to make use of the index for assessing initial clusters to select the clustering method to be used, allowing selecting the best procedure. |
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PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENTPROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENTProcedimento para Formação de Grupos de Empresas e para Construção de Índice de Avaliação dos AgrupamentosCluster AnalysisDiscriminant AnalysisGroup SizesIndex for Evaluating ClustersCluster AnalysisDiscriminant AnalysisGroup SizesIndex for Evaluating ClustersAnálise de agrupamentosAnálise discriminanteTamanhos de gruposÍndice de avaliação dos agrupamentos.The investigation of similarity patterns between companies can be accomplished by the formation of similarity groups. However, there is always doubt about the best way to build clusters. Thus, the objective of this work was to study some clustering methods and some possible group sizes, so as to obtain a good clustering. In addition, an index for evaluating the clusters formed was proposed. Six clustering methods and three group sizes were considered. After the application of cluster analysis, the groups formed were assessed by the discriminant analysis, the Kappa coefficient and an index developed to assess the initial clusters. Overall, it was found that the best option would be to apply the MW method with three groups and after, to apply the discriminant analysis to obtain an appropriate final number of companies per group. This procedure allowed obtaining groups relatively similar in terms of number of elements per group. Furthermore, an interesting alternative is to make use of the index for assessing initial clusters to select the clustering method to be used, allowing selecting the best procedure.The investigation of similarity patterns between companies can be accomplished by the formation of similarity groups. However, there is always doubt about the best way to build clusters. Thus, the objective of this work was to study some clustering methods and some possible group sizes, so as to obtain a good clustering. In addition, an index for evaluating the clusters formed was proposed. Six clustering methods and three group sizes were considered. After the application of cluster analysis, the groups formed were assessed by the discriminant analysis, the Kappa coefficient and an index developed to assess the initial clusters. Overall, it was found that the best option would be to apply the MW method with three groups and after, to apply the discriminant analysis to obtain an appropriate final number of companies per group. This procedure allowed obtaining groups relatively similar in terms of number of elements per group. Furthermore, an interesting alternative is to make use of the index for assessing initial clusters to select the clustering method to be used, allowing selecting the best procedure.A investigação de padrões de semelhança entre empresas pode ser realizada através da construção de grupos por similaridade. Contudo, geralmente se tem dúvidas quanto à melhor forma de construir os agrupamentos. Desse modo, tem-se o objetivo de estudar alguns métodos de agrupamentos e alguns possíveis tamanhos de grupos, de maneira a se obter um bom agrupamento. Além disso, se propõe um índice de avaliação dos agrupamentos formados. Consideraram-se seis métodos de agrupamentos e três tamanhos de grupos. Após a aplicação da análise de agrupamentos, foi realizada a avaliação dos agrupamentos formados através da análise discriminante, do coeficiente de concordância Kappa e de um índice desenvolvido para avaliar os agrupamentos iniciais. De forma geral, verificou-se que a melhor possibilidade seria aplicar o método MW com três grupos e, após, aplicar a análise discriminante até obter um número final adequado de empresas por grupo. Com este procedimento se obtêm grupos relativamente semelhantes em termos de número de elementos por grupo. Além disso, uma alternativa interessante é tomar uso do índice de avaliação dos agrupamentos iniciais para a escolha do método de agrupamento a ser utilizado, permitindo fazer a opção pelo melhor procedimento.ABEC2012-07-16info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/otherapplication/pdfhttps://www.revistasg.uff.br/sg/article/view/V7N1A610.7177/sg.2012.V7.N1.A6Sistemas & Gestão; v. 7 n. 1 (2012): Março/2012; 93-1021980-516010.7177/sg.2012.v7.n1reponame:Sistemas & Gestãoinstname:Universidade Federal Fluminense (UFF)instacron:UFFporhttps://www.revistasg.uff.br/sg/article/view/V7N1A6/V7N1A6Copyright (c) 2015 Sistemas & Gestãoinfo:eu-repo/semantics/openAccessSeidel, Enio JúniorOliveira, Marcelo Silva deTavares, BrunoAntonialli, Luis Marcelo2023-01-09T18:18:56Zoai:ojs.www.revistasg.uff.br:article/151Revistahttps://www.revistasg.uff.br/sgPUBhttps://www.revistasg.uff.br/sg/oai||sg.revista@gmail.com|| periodicos@proppi.uff.br1980-51601980-5160opendoar:2023-01-09T18:18:56Sistemas & Gestão - Universidade Federal Fluminense (UFF)false |
dc.title.none.fl_str_mv |
PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT Procedimento para Formação de Grupos de Empresas e para Construção de Índice de Avaliação dos Agrupamentos |
title |
PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT |
spellingShingle |
PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT Seidel, Enio Júnior Cluster Analysis Discriminant Analysis Group Sizes Index for Evaluating Clusters Cluster Analysis Discriminant Analysis Group Sizes Index for Evaluating Clusters Análise de agrupamentos Análise discriminante Tamanhos de grupos Í ndice de avaliação dos agrupamentos. |
title_short |
PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT |
title_full |
PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT |
title_fullStr |
PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT |
title_full_unstemmed |
PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT |
title_sort |
PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT |
author |
Seidel, Enio Júnior |
author_facet |
Seidel, Enio Júnior Oliveira, Marcelo Silva de Tavares, Bruno Antonialli, Luis Marcelo |
author_role |
author |
author2 |
Oliveira, Marcelo Silva de Tavares, Bruno Antonialli, Luis Marcelo |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Seidel, Enio Júnior Oliveira, Marcelo Silva de Tavares, Bruno Antonialli, Luis Marcelo |
dc.subject.por.fl_str_mv |
Cluster Analysis Discriminant Analysis Group Sizes Index for Evaluating Clusters Cluster Analysis Discriminant Analysis Group Sizes Index for Evaluating Clusters Análise de agrupamentos Análise discriminante Tamanhos de grupos Í ndice de avaliação dos agrupamentos. |
topic |
Cluster Analysis Discriminant Analysis Group Sizes Index for Evaluating Clusters Cluster Analysis Discriminant Analysis Group Sizes Index for Evaluating Clusters Análise de agrupamentos Análise discriminante Tamanhos de grupos Í ndice de avaliação dos agrupamentos. |
description |
The investigation of similarity patterns between companies can be accomplished by the formation of similarity groups. However, there is always doubt about the best way to build clusters. Thus, the objective of this work was to study some clustering methods and some possible group sizes, so as to obtain a good clustering. In addition, an index for evaluating the clusters formed was proposed. Six clustering methods and three group sizes were considered. After the application of cluster analysis, the groups formed were assessed by the discriminant analysis, the Kappa coefficient and an index developed to assess the initial clusters. Overall, it was found that the best option would be to apply the MW method with three groups and after, to apply the discriminant analysis to obtain an appropriate final number of companies per group. This procedure allowed obtaining groups relatively similar in terms of number of elements per group. Furthermore, an interesting alternative is to make use of the index for assessing initial clusters to select the clustering method to be used, allowing selecting the best procedure. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-07-16 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/other |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.revistasg.uff.br/sg/article/view/V7N1A6 10.7177/sg.2012.V7.N1.A6 |
url |
https://www.revistasg.uff.br/sg/article/view/V7N1A6 |
identifier_str_mv |
10.7177/sg.2012.V7.N1.A6 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://www.revistasg.uff.br/sg/article/view/V7N1A6/V7N1A6 |
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. 7 n. 1 (2012): Março/2012; 93-102 1980-5160 10.7177/sg.2012.v7.n1 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_ |
1798320142759755776 |