Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most them generally overlook significance weakly-supervised and fail preserve feature properties multiple views, thus resulting unsatisfactory performance. To address these issues, this paper, we propose a novel...

Multi-view clustering is an important research topic due to its capability utilize complementary information from multiple views. However, there are few methods consider the negative impact caused by certain views with unclear structures, resulting in poor multi-view performance. To address this drawback, we propose self-supervised discriminative feature learning for deep m...

In this paper, we present a new approach to exploring dynamic graphs. We have developed a new clustering algorithm for dynamic graphs which finds an ideal clustering for each time-step and links the clusters together. The resulting time-varying clusters are then used to define two visual representations. The first view is an overview that shows how clusters evolve over time and provides an inte...

Multiview clustering aims to improve clustering performance through optimal integration of information from multiple views. Though demonstrating promising performance in various applications, existing multiview clustering algorithms cannot effectively handle the view's incompleteness. Recently, one pioneering work was proposed that handled this issue by integrating multiview clustering and impu...

Most clustering algorithms produce a single clustering solution. Similarly, feature selection for clustering tries to find one feature subset where one interesting clustering solution resides. However, a single data set may be multi-faceted and can be grouped and interpreted in many different ways, especially for high dimensional data, where feature selection is typically needed. Moreover, diff...

Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features to form different views of it. Many app...

Due to the existence of various views or representations in many real-world data, multi-view learning has drawn much attention recently. Multi-view spectral clustering methods based on similarity matrixes or graphs are pretty popular. Generally, these algorithms learn informative graphs by directly utilizing original data. However, in the real-world applications, original data often contain noi...

The main aim of this paper is to demonstrate the performance of multi-classifiers fusion based on fuzzy clustering with ambiguity. The problem is seen from the multi-decision point of view (i.e. several classification modules). Each classification module is specialized on a particular region of the features space. These regions are obtained by fuzzy clustering and constitute the original data s...