Clustering, as an unsupervised learning, is aimed at discovering the natural grou**s of a
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …

Graph-oriented multi-view clustering methods have achieved impressive performances by
employing relationships and complex structures hidden in multi-view data. However, most of …

In recent research, metaheuristic strategies stand out as powerful tools for complex
optimization, capturing widespread attention. This study proposes the Educational …

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 …

Clustering ensemble integrates multiple base clustering results to obtain a consensus result
and thus improves the stability and robustness of the single clustering method. Since it is …

Consensus clustering, which learns a consensus clustering result from multiple weak base
results, has been widely studied. However, conventional consensus clustering methods only …

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

Multi-view spectral clustering can effectively reveal the intrinsic clustering structure among
data by performing clustering on the learned optimal embedding across views. Though …

Unsupervised feature selection is an important machine learning task and thus attracts
increasingly more attention. However, due to the absence of labels, unsupervised feature …

Consensus clustering provides an elegant framework to aggregate multiple weak clustering
results to learn a consensus one that is more robust and stable than a single result …