On semi-supervised clustering via multiobjective optimization

Abstract

Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. Semi-supervised clustering, in particular, explicitly integrates both information about the data distribution and about class memberships into the clustering process. In this paper, the potential of a multiobjective formulation of the semi-supervised clustering problem is explored, and two evolutionary multi-objective approaches to the problem are outlined. Experimental results demonstrate practical performance benefits of this methodology, including an improved classification performance and an increased robustness towards annotation errors.