General Adaptive Hypergraph Model for Coclustering

Xueyao Wang, Hongjun Wang, Tianrui Li

Abstract: Coclustering, as a crucial technique in data analysis, has proven effective in exploring the inherent data structures and revealing the synergistic effects of samples and features. However, most existing coclustering methods are limited to graph structures that primarily capture pairwise relationships between data points. In contrast, hyperedges in hypergraphs can connect multiple nodes, capturing complex associations between sets of points. Compared with the unidirectional clustering of hyperedges and nodes, bidirectional clustering of samples and features on hypergraphs presents a greater challenge. Moreover, unidirectional clustering overlooks the mutual dependencies between rows and columns, making it difficult to optimize clustering performance. Therefore, this article proposes a novel general and adaptive hypergraph model for coclustering (GAHGC), aiming to cocluster samples and features. It not only captures the similarity between samples but also considers the correlation information between samples and features. First, a general hypergraph construction strategy is proposed to overcome the limitation of existing methods that are often constrained by specific hypergraph data types. Next, an adaptive hypergraph optimization mechanism is designed to enhance coclustering performance. Finally, extensive comparative experiments on benchmark datasets demonstrate the effectiveness and superiority of the proposed model, further revealing the potential of hypergraph coclustering in capturing complex multilevel interactions.

External IDs:doi:10.1109/tcss.2025.3576131