In many clustering applications, real world data are often collected from multiple sources or with features from multiple channels. Thus, multi-view clustering has attracted much attention during the past few years. It is noteworthy that in many situations, in addition to the data samples, there is some side information describing the relation between instances, such as must-links and cannot-li...

In past decade, more and more data are collected from multiple sources or represented by multiple views, where different views describe distinct perspectives of the data. Although each view could be individually used for finding patterns by clustering, the clustering performance could be more accurate by exploring the rich information among multiple views. Several multi-view clustering methods ...

In multi-view clustering, different views may have different confidence levels when learning a consensus representation. Existing methods usually address this by assigning distinctive weights to different views. However, due to noisy nature of realworld applications, the confidence levels of samples in the same viewmay also vary. Thus considering a unified weight for a view may lead to suboptim...

With the increase of multi-view graph data, clustering (MVGC) that can discover hidden clusters without label supervision has attracted growing attention from researchers. Existing MVGC methods are often sensitive to given graphs, especially influenced by low quality i.e., they tend be limited homophily assumption. However, widespread real-world data hardly satisfy This gap limits performance e...

Although multi-view clustering (MVC) has achieved remarkable performance by integrating the complementary information of views, it is inefficient when facing scalable data. Proverbially, anchor strategy can mitigate such a challenge certain extent. However, unsupervised dynamic usually cannot obtain optimal anchors for MVC. The main reasons are that does not consider fairness different views an...