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

Abstract Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary
subject, and is referred to as, multi-sensor information fusion which was originated in the …

Ensembles for unsupervised outlier detection is an emerging topic that has been neglected
for a surprisingly long time (although there are reasons why this is more difficult than …

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 …

Ensemble clustering, also known as consensus clustering, is emerging as a promising
solution for multi-source and/or heterogeneous data clustering. The co-association matrix …

For obtaining the more robust, novel, stable, and consistent clustering result, clustering
ensemble has been emerged. There are two approaches in clustering ensemble …

It is highly likely that there is a partition that is judged by a stability measure as a bad one
while it contains one (or more) high quality cluster (s); and then it is totally neglected. So …

The ensemble clustering tries to aggregate a number of basic clusterings with the aim of
producing a more consistent, robust and well-performing consensus clustering result. The …

In spite of some attempts at improving the quality of the clustering ensemble methods, it
seems that little research has been devoted to the selection procedure within the fuzzy …

Since clustering ensemble was proposed, it has rapidly attracted much attention. This paper
makes an overview of recent research on clustering ensemble about generative mechanism …