Improvements to the scalability of multiobjective clustering

In previous work, we have introduced a novel and highly effective approach to data clustering, based on the explicit optimization of a partitioning with respect to two complementary clustering objectives [4, 5, 6]. In this paper, we make three modifications to the algorithm that improve its scalability to large data sets with high dimensionality and large numbers of clusters. Specifically, we introduce new initialization and mutation schemes that enable a more efficient exploration of the search space, and modify the null data model that is used as a basis for selecting the most significant solution from the Pareto front. The high performance of the resulting algorithm is demonstrated on a newly developed clustering test suite. © 2005 IEEE.