In the current research on multi-view clustering, the majority of methods have not adequately considered the diversity of multiple views nor focused on the high-order neighborhood information of the data. This has led to clustering results that lack accuracy and struggle to uncover the underlying information in datasets. To address these issues, we propose a multi-view clustering method based on diversity constraints and high-order information mining (MVCDCHO) . Firstly, we design a method for measuring diversity between views, utilizing diversity constraints to preserve the intersection features of the data while eliminating differing features across multiple views. Subsequently, we introduce a method for mining high-order information in views, requiring the intersection features of multiple views to approximate a mixed similarity graph, thereby extracting high-order information in data correlations that has been overlooked. Finally, we fuse the intersection features of multiple views into a consensus graph and employ spectral clustering to obtain the clustering target graph. Additionally, we design an alternating iterative method, iteratively learning to optimize the objective function. The experimental results show that the MVCDCHO algorithm has excellent performance on the normalized mutual information (NMI) , the adjusted Rand index (ARI) , and the clustering accuracy (ACC) . Theoretical analysis and experimental studies underscore the crucial role of multi-view diversity and high-order information in the MVCDCHO algorithm, providing evidence for its superiority.

[2024-03-07] Accepted Paper /doc/2023.12.0615/v1