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 …

This paper studies clustering of multi-view data, known as multi-view clustering. Among
existing multi-view clustering methods, one representative category of methods is the graph …

Multi-view clustering (MVC) methods aim to exploit consistent and complementary
information among each view and achieve encouraging performance improvement than …

As a challenging task in the field of unsupervised learning, incomplete multi-view clustering
can fully utilize multi-view information in the absence of partial views. Nevertheless, most …

Multi-view clustering methods group data into different clusters by discovering the
consensus in heterogeneous sources, which however becomes difficult when partial views …

The k-means algorithm is a widely used method that starts with an initial partitioning of the
data and then iteratively converges towards the local solution by reducing the Sum of …

Numerous works implemented multi-view clustering algorithms in document clustering. A
challenging problem in document clustering is the similarity metric. Existing multi-view …

In recent years, multi-view clustering research has attracted considerable attention because
of the rapidly growing demand for unsupervised analysis of multi-view data in practical …

Multi-view clustering algorithms based on graph learning have the ability to extract the
potential association between data samples, which has been a concern of many …

Multi-view clustering, which aims at dividing data with similar structures into their respective
groups, is a popular research subject in computer vision and machine learning. In recent …