Semi-Supervised Clustering via Constraints Self-Learning

So far, most of the semi-supervised clustering algorithms focus on finding a suitable partition that well satisfies the given constraints. However, insufficient supervisory information may lead to over-fitting results and unstable performance, especially on complicated data. To address this challenge, this paper attempts to solve the semi-supervised clustering problem by self-learning sufficient constraints. The essential motivation is that constraints can be learned from the local neighbor structures within appropriate feature spaces, and sufficient constraints can directly divide the data into clusters. Hence, we first present a constraint self-learning framework. It performs an expectation–maximization procedure iteratively between exploring a discriminant space and learning new constraints. Then, a constraint-based clustering algorithm is proposed by taking advantage of sufficient constraints. Experimental studies on various real-world benchmark datasets show that the proposed algorithm achieves promising performance and outperforms the state-of-the-art semi-supervised clustering algorithms.