Spectral clustering converts the data clustering problem to the graph cut problem. It is based
on graph theory. Due to the reliable theoretical basis and good clustering performance …

Ensemble clustering can obtain better and more robust results by fusing multiple base
clusterings, which has received extensive attention. Although many representative …

Semi-supervised feature selection methods jointly exploit the labelled and unlabelled
samples when selecting the features. Under the semi-supervised learning scenario, the …

Consensus clustering is gaining increasing attention for its high quality and robustness. In
particular, k-means-based Consensus Clustering (KCC) converts the usual computationally …

Abstract In Internet of Things (IoT), data sharing among different devices can improve
manufacture efficiency and reduce workload, and yet make the network systems be more …

Given a set of base clustering results, conventional bipartite graph-based ensemble
clustering methods typically require computing a sample-cluster similarity matrix from each …

Ensemble clustering (EC), utilizing multiple basic partitions (BPs) to yield a robust
consensus clustering, has shown promising clustering performance. Nevertheless, most …

Gravity-based clustering methods can effectively distinguish the differences between the
data points close to the center of a dataset and those on a class boundary. However, most …

The role of clustering in unsupervised fault diagnosis is significant, but different clustering
techniques can yield varied results and cause inevitable uncertainty. Ensemble clustering …

As a significant extension of classical clustering methods, ensemble clustering first
generates multiple basic clusterings and then fuses them into one consensus partition by …