Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm

Data Mining applied to the field of commercialization allows, among other aspects, to discover patterns of behavior in clients, which companies can use to create marketing strategies addressed to their different types of clients. This research focused on a database, the CRISP-DM methodology was appl...

Full description

Autores:
Silva, Jesus
Varela Izquierdo, Noel
Borrero López, Luz Adriana
Rojas Millán, Rafael Humberto
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/4835
Acceso en línea:
https://hdl.handle.net/11323/4835
https://repositorio.cuc.edu.co/
Palabra clave:
CRISP-DM methodology
Apriori algorithm
Association rules extraction
Data mining
SMEs
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm
dc.title.translated.spa.fl_str_mv Association rules extraction for customer segmentation in the SMEs sector using the apriori algorithm
title Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm
spellingShingle Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm
CRISP-DM methodology
Apriori algorithm
Association rules extraction
Data mining
SMEs
title_short Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm
title_full Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm
title_fullStr Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm
title_full_unstemmed Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm
title_sort Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm
dc.creator.fl_str_mv Silva, Jesus
Varela Izquierdo, Noel
Borrero López, Luz Adriana
Rojas Millán, Rafael Humberto
dc.contributor.author.spa.fl_str_mv Silva, Jesus
Varela Izquierdo, Noel
Borrero López, Luz Adriana
Rojas Millán, Rafael Humberto
dc.subject.spa.fl_str_mv CRISP-DM methodology
Apriori algorithm
Association rules extraction
Data mining
SMEs
topic CRISP-DM methodology
Apriori algorithm
Association rules extraction
Data mining
SMEs
description Data Mining applied to the field of commercialization allows, among other aspects, to discover patterns of behavior in clients, which companies can use to create marketing strategies addressed to their different types of clients. This research focused on a database, the CRISP-DM methodology was applied for the Data Mining process. The database used was that corresponding to the sector of SMEs and referring to customers and sales, the analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and on this model the grouping algorithms were applied: k -means, k-medoids, and SelfOrganizing Maps (SOM). To validate the result of the grouping algorithms and select 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.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-06-10T13:52:20Z
dc.date.available.none.fl_str_mv 2019-06-10T13:52:20Z
dc.date.issued.none.fl_str_mv 2019
dc.type.spa.fl_str_mv Artículo de revista
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identifier_str_mv 0000-2010
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/4835
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Al-hagery, M.A., 2015. Knowledge Discovery in the Data Sets of Hepatitis Disease for Diagnosis and Prediction to Support and Ser ve Community. Int. J. Comput. Electron. Res. 4, 118–125. [2] Amelec, V. (2015). Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Advanced Science Letters, 21(5), 1406-1408. [3] Fernandez-bassso, C., Ruiz, M.D., Martin-bautista, M.J., 2016. Extraction of Fuzzy association rules using Big Data technologies 11, 178– 185. https://doi.org/10.2495/DNE-V11-N3-178-185 [4] Lis-Gutiérrez M., Gaitán-Angulo M., Balaguera MI., Viloria A., Santander-Abril JE. (2018) Use of the Industrial Property System for New Creations in Colombia: A Departmental Analysis (2000–2016). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [5] Kulkarni, A.R., Mundhe, D.S.D., 2017. Data Mining Technique: An Implementation of Association Rule Mining in Healthcare. Iarjset 4, 62– 65. https://doi.org/10.17148/IARJSET.2017.4710 [6] Anuradha, K., Kumar, K.A., 2013. An E-Commerce application for Presuming Missing Items 4, 2636–2640. [7] Hwang, Y.M., Moon, J., Yoo, S., 2015. Developing A RFID-based food traceability system in Korea Ginseng Industry: Focused on the business process reengineering. Int. J. Control Autom. 8, 397–406. https://doi.org/10.14257/ijca.2015.8.4.36 [8] Vidhate, D., 2014. To improve Association Rule Mining using New Technique : Multilevel Relationship Algorithm towards Cooperative Learning 241–246. [9] Larose, D.T., Larose, C.D., 2014. Discovering Knowledge in Data. https://doi.org/10.1002/9781118874059 [10] Pickrahn, I., Kreindl, G., Müller, E., Dunkelmann, B., Zahrer, W., Cemper-Kiesslich, J., Neuhuber, F., 2017. Contamination incidents in the pre-analytical phase of forensic DNA analysis in Austria—Statistics of 17 years. Forensic Sci. Int. Genet. 31, 12–18. https://doi.org/10.1016/j.fsigen.2017.07.012 [11] DANE. 2018. Documento metodológico encuesta de desarrollo e innovación tecnológica en la industria Manufacturera. Bogotá: DANE. 43p. [12] Prajapati, D.J., Garg, S., Chauhan, N.C., 2017. Interesting Association Rule Mining with Consistent and Inconsistent Rul e Detection from Big Sales Data in Distributed Environment. Futur. Comput. Informatics J. 2, 19–30. https://doi.org/10.1016/j.fcij.2017.04.003 [13] Abdullah, M., Al-Hagery, H., 2016. Classifiers’ Accuracy Based on Breast Cancer Medical Data and Data Mining Techniques. Int. J. Adv. Biotechnol. Res. 7, 976–2612. [14] Varela Izquierdo N., Cabrera H.R., Lopez Carvajal G., Viloria A., Gaitán Angulo M., Henry MA. (2018) Methodology for the Reduction and Integration of Data in the Performance Measurement of Industries Cement Plants. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [15] Ban, T., Eto, M., Guo, S., Inoue, D., Nakao, K., Huang, R., 2015. A study on association rule mining of darknet big data. Int. Jt. Conf. Neural Networks 1–7. https://doi.org/10.1109/IJCNN.2015.7280818. [16] Witten, I.H., Frank, E., 2002. Data mining. ACM SIGMOD Rec. 31, 76. https://doi.org/10.1145/507338.507355 [17] Vo, B., Le, B., 2009. Fast Algorithm for Mining Generalized Association Rules 2, 1–12. [18] Kamatkar S.J., Tayade A., Viloria A., Hernández-Chacín A. (2018) Application of Classification Technique of Data Mining for Employee Management System. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [19] Khanali, H., 2017. A Survey on Improved Algorithms for Mining Association Rules 165, 8887. [20] Shorman, H.M. Al, Jbara, Y.H., 2017. An Improved Association Rule Mining Algorithm Based on Apriori and Ant Colony approaches 07, 18–23.
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spelling Silva, JesusVarela Izquierdo, NoelBorrero López, Luz AdrianaRojas Millán, Rafael Humberto2019-06-10T13:52:20Z2019-06-10T13:52:20Z20190000-2010https://hdl.handle.net/11323/4835Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Data Mining applied to the field of commercialization allows, among other aspects, to discover patterns of behavior in clients, which companies can use to create marketing strategies addressed to their different types of clients. This research focused on a database, the CRISP-DM methodology was applied for the Data Mining process. The database used was that corresponding to the sector of SMEs and referring to customers and sales, the analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and on this model the grouping algorithms were applied: k -means, k-medoids, and SelfOrganizing Maps (SOM). To validate the result of the grouping algorithms and select 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.Silva, Jesus-60750872-819f-4163-bbb8-c33aee0e2cf1-0Varela Izquierdo, Noel-0000-0001-7036-4414-600Borrero López, Luz Adriana-660b19b7-c6f3-4828-a318-9a0b0bb29206-0Rojas Millán, Rafael Humberto-0000-0002-4997-9040-600engProcedia Computer Sciencehttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2CRISP-DM methodologyApriori algorithmAssociation rules extractionData miningSMEsAssociation Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori AlgorithmAssociation rules extraction for customer segmentation in the SMEs sector using the apriori algorithmArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Al-hagery, M.A., 2015. Knowledge Discovery in the Data Sets of Hepatitis Disease for Diagnosis and Prediction to Support and Ser ve Community. Int. J. Comput. Electron. Res. 4, 118–125. [2] Amelec, V. (2015). Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Advanced Science Letters, 21(5), 1406-1408. [3] Fernandez-bassso, C., Ruiz, M.D., Martin-bautista, M.J., 2016. Extraction of Fuzzy association rules using Big Data technologies 11, 178– 185. https://doi.org/10.2495/DNE-V11-N3-178-185 [4] Lis-Gutiérrez M., Gaitán-Angulo M., Balaguera MI., Viloria A., Santander-Abril JE. (2018) Use of the Industrial Property System for New Creations in Colombia: A Departmental Analysis (2000–2016). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [5] Kulkarni, A.R., Mundhe, D.S.D., 2017. Data Mining Technique: An Implementation of Association Rule Mining in Healthcare. Iarjset 4, 62– 65. https://doi.org/10.17148/IARJSET.2017.4710 [6] Anuradha, K., Kumar, K.A., 2013. An E-Commerce application for Presuming Missing Items 4, 2636–2640. [7] Hwang, Y.M., Moon, J., Yoo, S., 2015. Developing A RFID-based food traceability system in Korea Ginseng Industry: Focused on the business process reengineering. Int. J. Control Autom. 8, 397–406. https://doi.org/10.14257/ijca.2015.8.4.36 [8] Vidhate, D., 2014. To improve Association Rule Mining using New Technique : Multilevel Relationship Algorithm towards Cooperative Learning 241–246. [9] Larose, D.T., Larose, C.D., 2014. Discovering Knowledge in Data. https://doi.org/10.1002/9781118874059 [10] Pickrahn, I., Kreindl, G., Müller, E., Dunkelmann, B., Zahrer, W., Cemper-Kiesslich, J., Neuhuber, F., 2017. Contamination incidents in the pre-analytical phase of forensic DNA analysis in Austria—Statistics of 17 years. Forensic Sci. Int. Genet. 31, 12–18. https://doi.org/10.1016/j.fsigen.2017.07.012 [11] DANE. 2018. Documento metodológico encuesta de desarrollo e innovación tecnológica en la industria Manufacturera. Bogotá: DANE. 43p. [12] Prajapati, D.J., Garg, S., Chauhan, N.C., 2017. Interesting Association Rule Mining with Consistent and Inconsistent Rul e Detection from Big Sales Data in Distributed Environment. Futur. Comput. Informatics J. 2, 19–30. https://doi.org/10.1016/j.fcij.2017.04.003 [13] Abdullah, M., Al-Hagery, H., 2016. Classifiers’ Accuracy Based on Breast Cancer Medical Data and Data Mining Techniques. Int. J. Adv. Biotechnol. Res. 7, 976–2612. [14] Varela Izquierdo N., Cabrera H.R., Lopez Carvajal G., Viloria A., Gaitán Angulo M., Henry MA. (2018) Methodology for the Reduction and Integration of Data in the Performance Measurement of Industries Cement Plants. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [15] Ban, T., Eto, M., Guo, S., Inoue, D., Nakao, K., Huang, R., 2015. A study on association rule mining of darknet big data. Int. Jt. Conf. Neural Networks 1–7. https://doi.org/10.1109/IJCNN.2015.7280818. [16] Witten, I.H., Frank, E., 2002. Data mining. ACM SIGMOD Rec. 31, 76. https://doi.org/10.1145/507338.507355 [17] Vo, B., Le, B., 2009. Fast Algorithm for Mining Generalized Association Rules 2, 1–12. [18] Kamatkar S.J., Tayade A., Viloria A., Hernández-Chacín A. (2018) Application of Classification Technique of Data Mining for Employee Management System. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [19] Khanali, H., 2017. A Survey on Improved Algorithms for Mining Association Rules 165, 8887. [20] Shorman, H.M. Al, Jbara, Y.H., 2017. An Improved Association Rule Mining Algorithm Based on Apriori and Ant Colony approaches 07, 18–23.PublicationORIGINALAssociation Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm.pdfAssociation Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm.pdfapplication/pdf445344https://repositorio.cuc.edu.co/bitstreams/fab2bfad-c73c-425f-a00c-af4bedd4a524/download2dad583292f1969042a2d86db4f6228bMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/2626cdca-99d7-4811-ad56-45950ef7b481/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/6ce56297-c795-41bb-87e5-fcc64d5558fb/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILAssociation Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm.pdf.jpgAssociation Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm.pdf.jpgimage/jpeg45771https://repositorio.cuc.edu.co/bitstreams/b1d6a837-e2fd-4322-8094-a6f054e8a283/download9b20ff1a68f0841a98c59303f25be83bMD55TEXTAssociation Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm.pdf.txtAssociation Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm.pdf.txttext/plain23936https://repositorio.cuc.edu.co/bitstreams/79ea3aa6-a9cf-48b3-9823-b40d91c4f8d7/download4f37d7bca687b41de4c13e0435007a39MD5611323/4835oai:repositorio.cuc.edu.co:11323/48352024-09-17 14:07:34.658http://creativecommons.org/licenses/by-nc-nd/4.0/open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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