MCSS: Discovering Consistently Determined Relation in Multi-view Clustering Based on Sample’s Stability

Multi-view clustering aims to discover the group knowledge in the widely existing multi-view data. Consistency is one of the fundamental factors for effectively handling the multi-view data clustering problem. It has been observed that there are two types of consistent relations, consistently ambiguous relations and consistently determined relations, which have negative and positive impacts on clustering, respectively. However, most of the existing multi-view clustering methods treat the consistency relation without distinction. In this paper, the sample’s stability in the sense of multi-view clustering is defined to recognize the consistently determined relations. Theoretically, it is revealed that the samples with higher stability have consistently determined relations with more other samples in all views, indicating a clear cluster structure. The rationality of the sample’s stability in multi-view is illustrated by experimental analysis. Further, a multi-view clustering method based on the sample’s stability (MCSS) is proposed. This method first calculates the sample’s stability and divides the samples into the stable region and unstable region. Then, the cluster structure in the stable region is discovered. Finally, the samples in the unstable region are assigned based on the pre-discovered cluster structure. The effectiveness of the proposed method based on sample’s stability is illustrated on nine benchmark multi-view data sets compared with ten multi-view clustering methods. The demo code is available at https://github.com/FeijiangLi/MCSS.