Effective semi-supervised graph clustering with pairwise constraints

Semi-supervised graph clustering with constraints has received considerable attention in the last decade. They use pre-given constraints to guide the clustering process and improve the performance. Nonetheless, most of related research works still face two main issues: (1) cannot-link problem: how to ensure that instances under the cannot-link constraint are located in different clusters as much as possible. (2) The high computational cost caused by eigendecomposition. To tackle these problems, this paper proposes a novel method for directly solving the clustering indicator matrix without high complexity computational steps. Then, we propose an effective and simple algorithm guided by the pre-given constraints, which is able to simultaneously optimize the associated multiple pairs of constraints, allowing the associated constraints to achieve the related co-optimal solution. Extensive experiments are conducted to verify that our approach significantly reduces the violation rate of the prior constraints and the clustering performance of the proposed method outperforms seven other state-of-the-art semi-supervised clustering methods on 16 real benchmark datasets.