Semi-supervised learning with centerless clustering

In unsupervised clustering tasks, incorporating pairwise constraints as prior information has become an effective approach to improve clustering quality. Building upon the efficient clustering algorithm k-sums, which unifies the frameworks of K-means and Ratio-cut, this paper introduces a novel semi-supervised clustering algorithm by integrating prior pairwise constraints. On one hand, the proposed method inherits the ability of k-sums to handle large-scale data and complex cluster structures with linear time complexity. On the other hand, it enhances clustering discriminability and robustness through constraint-based optimization under semi-supervised settings. Experimental results on multiple benchmark datasets demonstrate that the proposed method significantly outperforms existing mainstream semi-supervised clustering approaches in both clustering accuracy and computational efficiency, showing strong practical value and promising scalability.