Cluster ensembles have been shown to be better than any standard clustering algorithm at
improving accuracy and robustness across different data collections. This meta-learning …

This paper focuses on scalability and robustness of spectral clustering for extremely large-
scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra …

Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving
both the robustness and the stability of results from individual clustering methods. Weighted …

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 …

As one type of efficient unsupervised learning methods, clustering algorithms have been
widely used in data mining and knowledge discovery with noticeable advantages. However …

The rapid emergence of high-dimensional data in various areas has brought new
challenges to current ensemble clustering research. To deal with the curse of …

Ensemble Clustering (EC) is an important topic for data cluster analysis. It targets to
integrate multiple Basic Partitions (BPs) of a particular dataset into a consensus partition …

This article presents an unsupervised multi-view hierarchical embedding (UMHE) framework
to sufficiently reveal the intrinsic topical knowledge in social events. Event-oriented topics …

Clustering ensemble, also referred to as consensus clustering, has emerged as a method of
combining an ensemble of different clusterings to derive a final clustering that is of better …

Consensus clustering has been recently applied as a solution to the clustering problem. This
combines multiple clusterings of a set of objects into a single integrated clustering …