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 …

The class imbalance problem is one of the main challenges that hinders classifiers from
identifying unknown instances. When class distribution imbalance and class overlap coexist …

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

Existing clustering ensemble methods often directly integrate multiple weak base results to
obtain a consensus one which can improve the clustering performance. However, since the …

Although many existing co-association (CA) matrix-based ensemble clustering methods
have witnessed their superiority in unsupervised learning, but these methods still suffer the …

Ensemble clustering learns more accurate consensus results from a set of weak base
clustering results. This technique is more challenging than other clustering algorithms due to …

Employing a learning strategy analogous to human, from the easy to the difficult, better
classifiers could be achieved in a complicated pattern classification from different domains …

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

Anomaly detection is critical across domains, from cybersecurity to fraud prevention. Graphs,
adept at modeling intricate relationships, offer a flexible framework for capturing complex …

This paper addresses the critical gap in the understanding of the effects of various
configurations and feedback mechanisms on the performance of hybrid metaheuristics …