Clustering with Partition Level Side Information

Constrained clustering uses pre-given knowledge to improve the clustering performance. Among existing literature, researchers usually focus on Must-Link and Cannot-Link pairwise constraints. However, pairwise constraints not only disobey the way we make decisions, but also suffer from the vulnerability of noisy constraints and the order of constraints. In light of this, we use partition level side information instead of pairwise constraints to guide the process of clustering. Compared with pairwise constraints, partition level side information keeps the consistency within partial structure and avoids self-contradictory and the impact of constraints order. Generally speaking, only small part of the data instances are given labels by human workers, which are used to supervise the procedure of clustering. Inspired by the success of ensemble clustering, we aim to find a clustering solution which captures the intrinsic structure from the data itself, and agrees with the partition level side information as much as possible. Then we derive the objective function and equivalently transfer it into a K-mean-like optimization problem. Extensive experiments on several real-world datasets demonstrate the effectiveness and efficiency of our method compared to pairwise constrained clustering and consensus clustering, which verifies the superiority of partition level side information to pairwise constraints. Besides, our method has high robustness to noisy side information.