Ensemble clustering (EC) gains more and more attention because it can improve the
effectiveness and robustness of single clustering methods. A popular ensemble approach is …

Traditional clustering ensembles methods combine all obtained clustering results at hand.
However, we observe that it can often achieve a better clustering solution if only part of all …

The evidence accumulation model is an approach for collecting the information of base
partitions in a clustering ensemble method, and can be viewed as a kernel transformation …

Ensemble clustering has been a popular research topic in data mining and machine
learning. Despite its significant progress in recent years, there are still two challenging …

The clustering ensemble technique that integrates multiple clustering results can improve
the accuracy and robustness of the final clustering. In many clustering ensemble algorithms …

Ensemble clustering has attracted much attention for its robustness and effectiveness
compared to single clustering. As one of the representative methods, most co-association …

The aim of clustering ensemble is to combine multiple base partitions into a robust, stable
and accurate partition. One of the key problems of clustering ensemble is how to exploit the …

In this paper, we propose a new ensemble clustering approach termed ensemble clustering
using factor graph (ECFG). Compared to the existing approaches, our approach has three …

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 …

Ensemble Clustering (EC) has gained a great deal of attention throughout the fields of data
mining and machine learning, since it emerged as an effective and robust clustering …