Evaluation of clusters of credit cards holders

Detalhes bibliográficos
Autor(a) principal: Martins, M. C.
Data de Publicação: 2008
Outros Autores: Cardoso, M. G. M. S.
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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spelling 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
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dc.publisher.none.fl_str_mv Universidade Aberta
publisher.none.fl_str_mv Universidade Aberta
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