Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

The development of information gathering and extraction technology has led to the
popularity of multi-view data, which enables samples to be seen from numerous …

Multi-view subspace clustering aims to discover the hidden subspace structures from
multiple views for robust clustering, and has been attracting considerable attention in recent …

As one of the most important research topics in the unsupervised learning field, Multi-View
Clustering (MVC) has been widely studied in the past decade and numerous MVC methods …

Spectral clustering has attracted intensive attention in multimedia applications due to its
good performance on arbitrary shaped clusters and well-defined mathematical framework …

Although multi-view clustering has received widespread attention due to its far superior
performance to single-view clustering, it still faces the following issues:(1) high …

A clustering ensemble provides an elegant framework to learn a consensus result from
multiple prespecified clustering partitions. Though conventional clustering ensemble …

Recently, one-step clustering methods have shown good performance. However, very few
one-step approaches have addressed the multi-view case, where an instance may have …

Multi-view subspace clustering (MSC), assuming the multi-view data are generated from a
latent subspace, has attracted considerable attention in multi-view clustering. To recover the …

Recently, multi-view clustering has received much attention in the fields of machine learning
and pattern recognition. Spectral clustering for single and multiple views has been the …