Abstract A feature extraction method for customer purchase behavior based on genetic algorithm (GA) is proposed. Firstly, Tanimoto similarity is used to measure purchase behavior similarity between customers, and a clustering method based on genetic algorithm is designed to cluster customers who have similar purchase behavior in the same subpopulation. Then, an customer feature extraction method based on multi-population genetic algorithm is presented to find out knowledge from all kinds of subpopulation. To promote coevolution within the population and the quality of rule set, q-nearest neighbor replacement policy and local search are adopted. The proposed algorithm is validated by using real-world retail data and is compared with Apriori algorithm. Experimental results show that the proposed algorithm can efficiently yield condensed rule sets without generating frequent itemsets and is more flexible in rule form as well. Finally, the experimental results are analyzed in detail.
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