Evaluation of clusters of credit cards holders
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://ciencia.iscte-iul.pt/id/ci-pub-10480 http://hdl.handle.net/10071/13944 |
Resumo: | This work is focused on the evaluation of a clustering of credit card holders of a Portuguese financial organization, using a cross-validation procedure which is imported from supervised learning and used for evaluating results yielded by cluster analysis (an unsupervised technique). The proposed approach is conceived to deal with the particular sample characteristics – it handles a large data set and mixed (numerical and categorical) variables. This approach provides both the evaluation of the clustering solution and helps characterizing the clusters. Furthermore, it provides classification rules for new credit card holders. According to the obtained results, the internal stability is verified for a solution with five clusters. Finally, this work presents the profiles of the credit card holders’ clusters and suggests some possible strategies to study in each of them, in the business context. |
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Evaluation of clusters of credit cards holdersClusteringClustering evaluationInternal stabilityThis work is focused on the evaluation of a clustering of credit card holders of a Portuguese financial organization, using a cross-validation procedure which is imported from supervised learning and used for evaluating results yielded by cluster analysis (an unsupervised technique). The proposed approach is conceived to deal with the particular sample characteristics – it handles a large data set and mixed (numerical and categorical) variables. This approach provides both the evaluation of the clustering solution and helps characterizing the clusters. Furthermore, it provides classification rules for new credit card holders. According to the obtained results, the internal stability is verified for a solution with five clusters. Finally, this work presents the profiles of the credit card holders’ clusters and suggests some possible strategies to study in each of them, in the business context.Universidade Aberta2017-07-11T15:09:23Z2008-01-01T00:00:00Z20082017-07-11T15:08:52Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://ciencia.iscte-iul.pt/id/ci-pub-10480http://hdl.handle.net/10071/13944eng2182-1801Martins, M. C.Cardoso, M. G. M. S.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:RCAAP2023-11-09T17:48:36Zoai:repositorio.iscte-iul.pt:10071/13944Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:23:44.457531Repositório 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 |
Evaluation of clusters of credit cards holders |
title |
Evaluation of clusters of credit cards holders |
spellingShingle |
Evaluation of clusters of credit cards holders Martins, M. C. Clustering Clustering evaluation Internal stability |
title_short |
Evaluation of clusters of credit cards holders |
title_full |
Evaluation of clusters of credit cards holders |
title_fullStr |
Evaluation of clusters of credit cards holders |
title_full_unstemmed |
Evaluation of clusters of credit cards holders |
title_sort |
Evaluation of clusters of credit cards holders |
author |
Martins, M. C. |
author_facet |
Martins, M. C. Cardoso, M. G. M. S. |
author_role |
author |
author2 |
Cardoso, M. G. M. S. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Martins, M. C. Cardoso, M. G. M. S. |
dc.subject.por.fl_str_mv |
Clustering Clustering evaluation Internal stability |
topic |
Clustering Clustering evaluation Internal stability |
description |
This work is focused on the evaluation of a clustering of credit card holders of a Portuguese financial organization, using a cross-validation procedure which is imported from supervised learning and used for evaluating results yielded by cluster analysis (an unsupervised technique). The proposed approach is conceived to deal with the particular sample characteristics – it handles a large data set and mixed (numerical and categorical) variables. This approach provides both the evaluation of the clustering solution and helps characterizing the clusters. Furthermore, it provides classification rules for new credit card holders. According to the obtained results, the internal stability is verified for a solution with five clusters. Finally, this work presents the profiles of the credit card holders’ clusters and suggests some possible strategies to study in each of them, in the business context. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-01-01T00:00:00Z 2008 2017-07-11T15:09:23Z 2017-07-11T15:08:52Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://ciencia.iscte-iul.pt/id/ci-pub-10480 http://hdl.handle.net/10071/13944 |
url |
https://ciencia.iscte-iul.pt/id/ci-pub-10480 http://hdl.handle.net/10071/13944 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2182-1801 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Aberta |
publisher.none.fl_str_mv |
Universidade Aberta |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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