Ensemble Clustering Method via Robust Consensus Learning

Although ensemble clustering methods based on the co-association (CA) matrix have achieved considerable success, they still face the following challenges: (1) in the label space, the noise within the connective matrices and the structural differences between them are often neglected, and (2) the rich structural information inherent in the feature space is overlooked. Specifically, for each connective matrix, a symmetric error matrix is first introduced in the label space to characterize the noise. Then, a set of mapping models is designed, each of which processes a denoised connective matrix to recover a reliable consensus matrix. Moreover, multi-order graph structures are introduced into the feature space to enhance the expressiveness of the consensus matrix further. To preserve a clear cluster structure, a theoretical rank constraint with a block-diagonal enhancement property is imposed on the consensus matrix. Finally, spectral clustering is applied to the refined consensus matrix to obtain the final clustering result. Experimental results demonstrate that ECM-RCL achieves superior clustering performance compared to several state-of-the-art methods.