Abstract Complex Fermatean fuzzy sets (CFFSs) integrate the ideas of complex fuzzy sets
and Fermatean fuzzy sets, where the membership, non-membership, and hesitancy degrees …

As a core paradigm of the evidential clustering algorithm, evidential c-means (ECM) offers a
more flexible credal partition to characterize uncertainty and imprecision in cluster …

How to effectively deal with uncertain and imprecise information in decision making is a
complex task. Dempster-Shafer evidence theory (DSET) is widely used for handling such …

Currently, many studies have developed distance or divergence measures between
intuitionistic fuzzy sets (IFSs) and interval-valued fuzzy sets (IvFSs). As a generalization of …

In the realm of information fusion, clustering stands out as a common subject and is
extensively applied across various fields. Evidential clustering, an increasingly popular …

Belief functions theory (BFT) plays a critical role in addressing uncertainty and imprecision in
multi-source data fusion. Unfortunately, the application of Dempster's rule in BFT often …

Incomplete multi-view data classification poses significant challenges due to the common
issue of missing views in real-world scenarios. Despite advancements, existing methods …

Multi-view clustering has become increasingly pervasive and prominent as multiple sources
often provide different representations of information. However, existing multi-view clustering …

Multi-view clustering remains a challenging task due to the potential overlap of clusters and
variability across different views, which causes uncertainty and imprecision in cluster …

Evidence theory (ET) has gained significant attention in various fields due to its advantages
over probability theory. However, highly conflicting evidence can sometimes yield …