In the last decades, a wide portfolio of Feature Weighting (FW) methods have been
proposed in the literature. Their main potential is the capability to transform the features in …

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 …

Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving
both the robustness and the stability of results from individual clustering methods. Weighted …

Multi-view clustering has attracted substantial attention thanks to its ability to integrate
information from diverse views. However, the existing methods can only generate hard or …

Fuzzy c-means (FCM) clustering had been extended for handling multi-view data with
collaborative idea. However, these collaborative multi-view FCM treats multi-view data …

Although graph-inspired clustering methods have achieved impressive success in the area
of multi-view data analysis, current methods still face several challenges. First, classical …

Climate change and intensified human activities are exacerbating the frequency and
severity of extreme precipitation events, necessitating more precise and timely flood risk …

Since the data in each view may contain distinct information different from other views as
well as has common information for all views in multi-view learning, many multi-view …

The rapid development in information technology makes it easier to collect vast numbers of
data through the cloud, internet and other sources of information. Multiview clustering is a …

Multi-view clustering can explore consistent information from different views to guide
clustering. Most existing works focus on pursuing shallow consistency in the feature space …