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Título : Customer Segmentation Using Multiple Instance Clustering and Purchasing Behaviors
Autor : Fuentes, Ivett
Nápoles, Gonzalo
Arco, Leticia
Vanhoof, Koen
Palabras clave : MULTIPLE INSTANCE CLUSTERING;CUSTOMER PURCHASING BEHAVIORS;DECISION SUPPORT SYSTEMS
Fecha de publicación : 2018
Editorial : Springer
Citación : Fuentes I., Nápoles G., Arco L., Vanhoof K. (2018) Customer Segmentation Using Multiple Instance Clustering and Purchasing Behaviors. In: Hernández Heredia Y., Milián Núñez V., Ruiz Shulcloper J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science, vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_22
Resumen : On-line companies usually maintain complex information systems for capturing records about Customer Purchasing Behaviors (CPBs) in a cost-effective manner. Building prediction models from this data is considered a crucial step of most Decision Support Systems used in business informatics. Segmentation of similar CPB is an example of such an analysis. However, existing methods do not consider a strategy for quantifying the interactions between customers taking into account all entities involved in the problem. To tackle this issue, we propose a customer segmentation approach based on their CPB profile and multiple instance clustering. More specifically, we model each customer as an ordered bag comprised of instances, where each instance represents a transaction (order). Internal measures and modularity are adopted to evaluate the resultant segmentation, thus supporting the reliability of our model in business marketing analysis.
URI : https://repositorio.uci.cu/jspui/handle/123456789/9477
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