Multiobjective Clustering with Metaheuristic Optimization Technology

We develop a metaheuristic procedure for multiobjective clustering problems. Our goal is to find good approximations of the efficient frontier for this class of problems and provide a means for improving decision making in multiple areas of application and in particular those related to marketing. The procedure is based on the tabu and scatter search methodologies. Clustering problems have been the subject of numerous studies; however, most of the work has focused on single-objective problems. Clustering using multiple criteria and/or multiple data sources has received limited attention in the OR and marketing literature. Our procedure is general and tackles several problems classes within this area of combinatorial data analysis. We conduct extensive experimentation with both artificial and real data (in a marketing-segmentation problem) to show the effectiveness of the proposed procedure.