Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm
Customer´s segmentation is used as a marketing differentiation tool which allows organizations to understand their customers and build differentiated strategies. This research focuses on a database from the SMEs sector in Colombia, the CRISP-DM methodology was applied for the Data Mining process. Th...
- Autores:
-
silva d, jesus g
Gaitan-Angulo, Mercedes
Cabrera, Danelys
Kamatkar, Sadhana J.
Martínez Caraballo, Hugo
Martínez Ventura, Jairo Luis
Virviescas Peña, John Anderson
De la Hoz Hernández, Juan David
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5413
- Acceso en línea:
- https://hdl.handle.net/11323/5413
https://repositorio.cuc.edu.co/
- Palabra clave:
- Data mining
Apriori algorithm
Dates product
Association rules
Hidden patterns extraction
Consumer's loyalty
- Rights
- openAccess
- License
- http://creativecommons.org/publicdomain/zero/1.0/
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|
dc.title.spa.fl_str_mv |
Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm |
title |
Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm |
spellingShingle |
Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm Data mining Apriori algorithm Dates product Association rules Hidden patterns extraction Consumer's loyalty |
title_short |
Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm |
title_full |
Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm |
title_fullStr |
Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm |
title_full_unstemmed |
Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm |
title_sort |
Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm |
dc.creator.fl_str_mv |
silva d, jesus g Gaitan-Angulo, Mercedes Cabrera, Danelys Kamatkar, Sadhana J. Martínez Caraballo, Hugo Martínez Ventura, Jairo Luis Virviescas Peña, John Anderson De la Hoz Hernández, Juan David |
dc.contributor.author.spa.fl_str_mv |
silva d, jesus g Gaitan-Angulo, Mercedes Cabrera, Danelys Kamatkar, Sadhana J. Martínez Caraballo, Hugo Martínez Ventura, Jairo Luis Virviescas Peña, John Anderson De la Hoz Hernández, Juan David |
dc.subject.spa.fl_str_mv |
Data mining Apriori algorithm Dates product Association rules Hidden patterns extraction Consumer's loyalty |
topic |
Data mining Apriori algorithm Dates product Association rules Hidden patterns extraction Consumer's loyalty |
description |
Customer´s segmentation is used as a marketing differentiation tool which allows organizations to understand their customers and build differentiated strategies. This research focuses on a database from the SMEs sector in Colombia, the CRISP-DM methodology was applied for the Data Mining process. The analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and the following grouping algorithms were applied on this model: k -means, k-medoids, and Self-Organizing Maps (SOM). For validating the result of the grouping algorithms and selecting the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers, so determining association according to loyalty. |
publishDate |
2019 |
dc.date.accessioned.none.fl_str_mv |
2019-10-07T13:33:31Z |
dc.date.available.none.fl_str_mv |
2019-10-07T13:33:31Z |
dc.date.issued.none.fl_str_mv |
2019-07-19 |
dc.type.spa.fl_str_mv |
Pre-Publicación |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_816b |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/preprint |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ARTOTR |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_816b |
status_str |
acceptedVersion |
dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/5413 |
dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
url |
https://hdl.handle.net/11323/5413 https://repositorio.cuc.edu.co/ |
identifier_str_mv |
Corporación Universidad de la Costa REDICUC - Repositorio CUC |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.publisher.spa.fl_str_mv |
Universidad de la Costa |
institution |
Corporación Universidad de la Costa |
bitstream.url.fl_str_mv |
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silva d, jesus gGaitan-Angulo, MercedesCabrera, DanelysKamatkar, Sadhana J.Martínez Caraballo, HugoMartínez Ventura, Jairo LuisVirviescas Peña, John AndersonDe la Hoz Hernández, Juan David2019-10-07T13:33:31Z2019-10-07T13:33:31Z2019-07-19https://hdl.handle.net/11323/5413Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Customer´s segmentation is used as a marketing differentiation tool which allows organizations to understand their customers and build differentiated strategies. This research focuses on a database from the SMEs sector in Colombia, the CRISP-DM methodology was applied for the Data Mining process. The analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and the following grouping algorithms were applied on this model: k -means, k-medoids, and Self-Organizing Maps (SOM). For validating the result of the grouping algorithms and selecting the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers, so determining association according to loyalty.silva d, jesus g-will be generated-orcid-0000-0003-3555-9149-0Gaitan-Angulo, Mercedes-will be generated-orcid-0000-0002-8248-8788-600Cabrera, Danelys-will be generated-orcid-0000-0002-9486-9764-600Kamatkar, Sadhana J.Martínez Caraballo, HugoMartínez Ventura, Jairo Luis-will be generated-orcid-0000-0002-6607-3515-600Virviescas Peña, John Anderson-will be generated-orcid-0000-0002-2917-3816-600De la Hoz Hernández, Juan David-will be generated-orcid-0000-0002-4025-8538-600engUniversidad de la Costahttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Data miningApriori algorithmDates productAssociation rulesHidden patterns extractionConsumer's loyaltyAssociation rule mining for customer segmentation in the SMEs sector using the apriori algorithmPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALAssociation Rule Mining for Customer Segmentation in the SMEs Sector using the Apriori Algorithm.pdfAssociation Rule Mining for Customer Segmentation in the SMEs Sector using the Apriori Algorithm.pdfapplication/pdf72334https://repositorio.cuc.edu.co/bitstreams/844bfb71-f532-4ecc-bb2f-0afaa3dd3aa1/download9c7d1633a767100904f4ef7077bb5bb1MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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