Improvements to the scalability of multiobjective clustering
Julia Handl, Joshua D. Knowles · 2005
In previous work, the authors 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 (Handl, et. al., 2004, 2005). In this paper, three modifications were made to the algorithm that improved its scalability to large data sets with high dimensionality and large numbers of clusters. Specifically, new initialization and mutation schemes that enable a more efficient exploration of the search space were introduced, and the null data model that is used as a basis for selecting the most significant solution from the Pareto front was modified. The high performance of the resulting algorithm is demonstrated on a newly developed clustering test suite.