Semi-supervised clustering with pairwise and size constraints

In recent years, semi-supervised clustering receives considerable attention in the pattern recognition and data mining communities. This type of clustering algorithms takes advantage of partial prior knowledge, and significant improved performance beyond traditional unsupervised clustering algorithms is observed. In general, the partial prior knowledge is mainly in the form of pairwise constraints, which specify whether point pairs should be in the same cluster or in different clusters. Moreover, some other forms of constraints also attract research interests, for example, the balance constraint or the size constraint. However, it is also important to consider different types of constraints simultaneously, since different types of prior knowledge might have their own bias when considered separately. In this paper, we propose an improved algorithm to incorporate the pairwise and size constraints into a unified framework. Experiments on several benchmark data sets demonstrate that the proposed unified algorithm outperforms previous approaches under a variety of different conditions, which demonstrates that judicious integration of different types of constraints can result in improved performance than in those cases where only a single kind of constraint is used.