High utility itemset mining using binary differential evolution: An application to customer segmentation

摘要

In this paper, high utility itemset mining (HUIM) algorithms driven by Binary Differential Evolution (BDE) and an Adaptive Binary Differential Evolution (ABDE) are proposed separately. These are compared with the HUIM algorithms of (i) Binary Particle Swarm Optimization, (ii) Genetic Algorithm and (iii) a two-phase HUIM found in the literature. The proposed HUIM algorithms are applied on seven datasets, where OnlineRetail dataset is a reallife dataset and the objective there is to segment high value customers based on monetary value. From the results, it is clear that BDE based HUIM outperformed the extant algorithms in literature and also the ABDE HUIM algorithm on all datasets with respect to the maximum number of itemsets mined.