In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete clustering, view-missing increases difficulty learning common representations from different To address challenge, propose a novel framework, which incorporates cross-view relation transfer and fusion learning. Specifically, based consistency existing in data, devise transfer-based comple...

Graph-oriented methods have been widely adopted in multi-view clustering because of their efficiency learning heterogeneous relationships and complex structures hidden data. However, existing are typically investigated based on a Euclidean structure instead more suitable manifold topological structure. Hence, it is expected that will be to carry out intrinsic similarity learning. In this paper,...

Multi-view data becomes prevalent nowadays because more and more data can be collected from various sources. Each data set may be described by different set of features, hence forms a multi-view data set or multi-view data in short. To find the underlying pattern embedded in an unlabelled multiview data, many multi-view clustering approaches have been proposed. Fuzzy clustering in which a data ...

In recent years, combining multiple sources or views of datasets for data clustering has been a popular practice for improving clustering accuracy. As different views are different representations of the same set of instances, we can simultaneously use information from multiple views to improve the clustering results generated by the limited information from a single view. Previous studies main...

Model-based clustering techniques have been widely used and have shown promising results in many applications involving complex data. This paper presents a unified framework for probabilistic model-based clustering based on a bipartite graph view of data and models that highlights the commonalities and differences among existing model-based clustering algorithms. In this view, clusters are repr...

Eisen’s tree view is a useful tool for clustering and displaying of microarray gene expression data. In Eisen’s tree view system, a hierarchical method is used for clustering data. However, some useful information in gene expression data may not be well drawn when a hierarchical clustering is directly used in Eisen’s tree view. In this paper, we embed the similarity-based clustering method (SCM...