Ensemble clustering is aimed at obtaining a robust consensus result from a set of weak base
clusterings. Most existing methods rely on a co-association (CA) matrix that describes the …

Ensemble clustering refers to learning a robust and accurate consensus result from a
collection of base clustering results. Despite extensive research on this topic, it remains …

Deep self-expressiveness-based subspace clustering methods have demonstrated
effectiveness. However, existing works only consider the attribute information to conduct the …

Multi-view clustering (MVC) focuses on adaptively partitioning data from diverse sources into
the respective groups and has been widely studied under the assumption of complete data …

Ensemble clustering has emerged as a powerful framework for analyzing heterogeneous
and complex data. Despite the abundance of existing schemes, co-association matrix-based …

Ensemble clustering has shown its promising ability in fusing multiple base clusterings into a
probably better and more robust clustering result. Typically, the co-association matrix based …

Clustering ensemble often provides robust and stable results without accessing original
features of data, and thus has been widely studied. The conventional clustering ensemble …

Existing deep embedding clustering methods fail to sufficiently utilize the available off-the-
shelf information from feature embeddings and cluster assignments, limiting their …

Ensemble clustering based on co-association matrices integrates multiple connective
matrices from base clusterings to achieve superior results. However, these methods …

The co-association (CA) matrix that describes connection relationship between instances is
of importance for ensemble clustering. Existing ensemble clustering methods demonstrate …