PROCEDURE FOR THE FORMATION OF GROUPS OF COMPANIES AND FOR BUILDING THE INDEX FOR CLUSTER ASSESSMENT
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 obt...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2012 |
| País: | Brasil |
| Recursos: | Universidade Federal Fluminense (UFF) |
| Repositório: | Sistemas & Gestão |
| Idioma: | português |
| OAI Identifier: | oai:ojs.www.revistasg.uff.br:article/151 |
| Acesso em linha: | https://www.revistasg.uff.br/sg/article/view/V7N1A6 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Cluster Analysis Discriminant Analysis Group Sizes Index for Evaluating Clusters Análise de agrupamentos Análise discriminante Tamanhos de grupos Í ndice de avaliação dos agrupamentos. |
| 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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