Improved active constraint selection for partial constrained clustering algorithms

Semi-supervised clustering algorithms introduce partial knowledge into traditional unsupervised methods and generally improve results. Partial constrained clustering is one of the main kinds of semi-supervised clustering algorithms. Notably, constraints selected at random might probable bring only trivial improvement. To improve both the effectiveness and the efficiency of the partial constrained clustering algorithms, active selection for constraints is important. However, there are only few studies on the selection of active constraints. In view of this problem, in this paper we propose an improved selection approach of active constraints for partial constrained clustering algorithms. Compared to the state-of-the-art Explore and Consolidate approach, Experiments on a number of public benchmark data sets show that (i) our approach can find more informational constraints for partial constrained clustering algorithms and bring encouraging improvement; and (ii) our approach can find out constraints distributed among all the classes in investigated data sets quickly, which shows that our approach can be used in more occasions when only small numbers of constraints are allowed.