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...

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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
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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
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dc.publisher.spa.fl_str_mv Universidad de la Costa
institution Corporación Universidad de la Costa
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spelling 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. 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