Customer Segmentation Using Multiple Instance Clustering and Purchasing Behaviors

Ivett Fuentes*, Gonzalo Nápoles, Leticia Arco, Koen Vanhoof

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review


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.
Original languageEnglish
Title of host publication Progress in Artificial Intelligence and Pattern Recognition
Number of pages8
Publication statusPublished - 2018
Externally publishedYes


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