Clustering data with both numeric and categorical attributes is of great importance as such
data are ubiquitous in real-world problems. Multi-view learning approaches have proven to …

With the emergence of multi-view data, many multi-view clustering methods have been
developed due to the effectiveness of exploiting the complementary information of multi-view …

With the widespread deployment of sensors and the Internet-of-Things, multi-view data has
become more common and publicly available. Compared to traditional data that describes …

In multiview learning, it is essential to assign a reasonable weight to each view according to
the view importance. Thus, for multiview clustering task, a wise and elegant method should …

Multi-view clustering, which explores complementary information between multiple distinct
feature sets for better clustering, has a wide range of applications, eg, knowledge …

Multi-view spectral clustering has gained considerable attention due to its potential to
enhance clustering performance. Although many methods have shown promising results …

Multi-view clustering (MVC) can integrate the complementary information between different
views to remarkably improve clustering performance. However, the existing methods suffer …

In recent decades, tremendous emerging techniques thrive the artificial intelligence field due
to the increasing collected data captured from multiple sensors. These multi-view data …

Multiview data are widespread in real-world applications, and multiview clustering is a
commonly used technique to effectively mine the data. Most of the existing algorithms …

Multi-view clustering aims to incorporate complementary information from different data
views for more effective clustering. However, it is difficult to obtain the true categories of data …