This study investigates the problem of multi-view subspace clustering, goal which is to explore underlying grouping structure data collected from different fields or measurements. Since do not always comply with linear models in many real-world applications, most existing clustering methods based on shallow may fail practice. Furthermore, graph information usually ignored methods. To address af...

As a class of effective methods for incomplete multi-view clustering, graph-based algorithms have recently drawn wide attention. However, most them could use further improvement regarding the following aspects. First, in some models, all views are forced to share common similarity graph regardless severe consistency degeneration due views. Next, construction and cluster analysis sometimes perfo...

This paper explains clustering, a data mining technique which mainly focuses on the grouping of similar objects, in this technique many researchers have done the work in different areas like web document clustering, text clustering and different models of clustering like connectivity models(hierarchical clustering), centroid model (k-means),distribution models, density models, subspace model, g...

Many real-world datasets are comprised of different representations or views which often provide information complementary to each other. To integrate information from multiple views in the unsupervised setting, multiview clustering algorithms have been developed to cluster multiple views simultaneously to derive a solution which uncovers the common latent structure shared by multiple views. In...

We present a new view of clustering and segmentation by pairwise similarities. We interpret the similarities as edge ows in a Markov random walk and study the eigenvalues and eigenvectors of the walk's transition matrix. This view shows that spectral methods for clustering and segmentation have a probabilistic foundation. We prove that the Normalized Cut method arises naturally from our framewo...

Automatic social circle detection in ego-networks is becoming a fundamentally important task for social network analysis, which can be used for privacy protection or interest group recommendation. So far, most studies focused on how to detect overlapping circles or how to perform detection using both network structure and its node profiles. This paper asks an orthogonal research question: how t...

Representation learning and unsupervised learning are two central topics of machine learning and signal processing. Deep learning is one of the most effective unsupervised representation learning approach. The main contributions of this paper to the topics are as follows. (i) We propose to view the representative deep learning approaches as special cases of the knowledge reuse framework of clus...

When multiple views of data are available for a set of subjects, co-clustering aims to identify subject clusters that agree across the different views. We explore the problem of co-clustering when the underlying clusters exist in different subspaces of each view. We propose a proximal alternating linearized minimization algorithm that simultaneously decomposes multiple data matrices into sparse...