Graph-based multi-view clustering methods aim to explore the partition patterns by utilizing a similarity graph. However, many existing construct consensus graph based on original space, which may result in lack of information underlying low-dimensional space. Additionally, these often fail effectively handle noise present To address issues, novel graph-based method combines spectral embedding,...

Clustering data with both numeric and categorical attributes is of great importance as such are ubiquitous in real-world problems. Multi-view learning approaches have proven to be more effective having better generalisation ability compared single-view many However, most the existing clustering algorithms developed for mixed single-view. In this research, we propose a novel multi-view algorithm...

Multi-view data are becoming common in real-world applications and many multi-view clustering algorithms have thus been proposed. The existing usually focus on the cooperation of different visible views original space but neglect influence hidden information among these views, or they only consider views. therefore not efficient since available is fully exploited, particularly otherness consist...

Clustering is inherently a process of exploratory data analysis. It has attracted more attention recently because much real-world consists multiple representations or views. However, it becomes increasingly problematic when dealing with large and heterogeneous data. worth noting that several approaches have been developed to increase computational efficiency, although most them some drawbacks: ...

As one of the most important research topics in unsupervised learning field, Multi-View Clustering (MVC) has been widely studied past decade and numerous MVC methods have developed. Among these methods, recently emerged Graph Neural Networks (GNN) shine a light on modeling both topological structure node attributes form graphs, to guide unified embedding clustering. However, effectiveness exist...