Using Multi-Objective Optimization for the Selection of Ensemble Members

In this paper we propose a clustering process which uses a multi-objective evolution to select a set of diverse clusterings. The selected clusterings are then combined using a consensus method. This approach is compared to a clustering process where no selection is applied. We show that careful selection of input ensemble members can improve the overall quality of the final clustering. Our algorithm provides more stable clustering results and in many cases overcomes the limitations of base algorithms.