Active constraints selection based on density peak

Semi-supervised clustering which aims to integrate side information to improve the performance of clustering process, has received a lot of attentions in research community. Generally, there are two kinds of side information called seed (labelled data) and constraint (must-link, cannot-link). By integrating information provided by the user or domain expert, the semi-supervised clustering can produce expected results of users. In fact, clustering results usually depend on side information provided, so different side information will produce different results. In some cases, the performance of clustering may decrease if the side information is not carefully chosen. This paper addresses the problem of selecting good constraints for semi-supervised clustering algorithms. For this purpose, we propose an active learning algorithm for the constraints collection task, which relies on the min-max algorithm and peaks estimation based on density score. Experiments conducted on some real data sets from UCI show the effectiveness of our approach.