Applying particle swarm optimization and honey bee mating optimization in developing an intelligent market segmentation system

Abstract

Data mining has been successfully applied in many fields to find useful information stored in vast databases. Market segmentation, which segments data into homogenous clusters by using cluster analysis, is among the most important of the applications in data mining. In this study, we propose a clustering system which integrates particle swarm optimization and honey bee mating optimization methods (PSHBMO). Simulations for a benchmark test function show that our proposed method is better equipped to find the global optimum than other well-known clustering algorithms. Finally, the proposed clustering system is applied to a real-world consumer electronic company to perform market segmentation via the RFM model.