Clustering problems in a multiobjective framework

We propose a new algorithm using tabu search to deal with biobjective clustering problems. A cluster is a collection of records that are similar to one other and dissimilar to records in other clusters. Clustering has applications in VLSI design, protein-protein interaction networks, data mining and many others areas. Clustering problems have been subject of numerous studies; however, most of the work has focused on single-objective problems. In the context of multiobjective optimization our aim is to nd a good approximation to the Pareto front and provide a method to make decisions. As an application problem we present the zoning problem by allowing the optimization of two objectives.