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 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.

• 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: http://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/