Multi-view clustering methods have been a focus in recent years because of their superiority performance. However, typical traditional multi-view algorithms still shortcomings some aspects, such as removal redundant information, utilization various views and fusion features. In view these problems, this paper proposes new method, low-rank subspace based on adaptive graph regularization. We cons...

Multi-view clustering, which aims to cluster datasets with multiple sources of information, has a wide range of applications in the communities of data mining and pattern recognition. Generally, it makes use of the complementary information embedded in multiple views to improve clustering performance. Recent methods usually find a low-dimensional embedding of multi-view data, but often ignore s...

With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. Although recently, multi-view clustering (MVC) methods have been developed rap...

Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data. This paper presents an approach to multi-view subspace clustering that learns a joint subspace representation by constructing affinity matrix shared among all views. Relyi...

The plenty information from multiple views data as well as the complementary information among different views are usually beneficial to various tasks, e.g., clustering, classification, de-noising. Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace. To recover the underlying subspace structure, the success of the sparse and/or low-r...

the ways of placing decision making units (dmus) in certain clusters are found as a subject in statistics, these ways usually are heuristic. the proposed clustering approach in this article considers preferences of dmus. this study applies data envelopment analysis (dea) dmus are clustered by solving multi-objective linear problem (molp) and by considering preferences of each dmu at production ...