Semi-Supervised Clustering Algorithms Through Active Constraints

Pairwise constraints improve clustering performance in constraint-based clustering issues, especially since they are applicable. However, randomly choosing these constraints may be adverse and minimize accuracy. To address the problem of random choosing pairwise constraints, an active learning method is used to identify the most informative constraints, which are then selected by the active learning technique. In this research, we replaced random selection with an active learning strategy. We provide a semi-supervised selective affinity propagation clustering approach with active constraints, which combines the affinity propagation (AP) clustering algorithm with prior information to improve semi-supervised clustering performance. Based on the neighborhood concept, we select the most informative constraints where neighborhoods include labelled examples of various clusters. The experimental results on eight real datasets demonstrate that the proposed method in this paper outperforms other baseline methods and that it can improve clustering performance significantly.