Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …

A panoply of multi-view clustering algorithms has been developed to deal with prevalent
multi-view data. Among them, spectral clustering-based methods have drawn much attention …

Multiview clustering has gained increasing attention recently due to its ability to deal with
multiple sources (views) data and explore complementary information between different …

Learning graphs from data automatically have shown encouraging performance on
clustering and semisupervised learning tasks. However, real data are often corrupted, which …

Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning
in kernel space has shown impressive performance on a number of benchmark data sets …

Clustering with multiview data is becoming a hot topic in data mining, pattern recognition,
and machine learning. In order to realize an effective multiview clustering, two issues must …

Multi-view clustering (MVC) enhances the clustering performance of data by combining
correlation information from different views. However, most existing MVC approaches …

In recent years, multi-view clustering algorithms have shown promising performance by
combining multiple sources or views of datasets. A problem that has not been addressed …