Clustering ensembles can obtain more superior final results by combining multiple different
clustering results. The qualities of the points, clusters, and partitions play crucial roles in the …

Existing clustering ensemble selection methods adopt internal and external evaluation
indexes to measure the quality and diversity of base clusterings. The significance of base …

Ensemble clustering has emerged as a combination of several basic clustering algorithms to
achieve high quality final clustering. However, this technique is challenging due to the …

Clustering as a major task in data mining is responsible for discovering hidden patterns in
unlabeled datasets. Finding the best clustering is also considered as one of the most …

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 …

A clustering ensemble aims to combine multiple clustering models to produce a better result
than that of the individual clustering algorithms in terms of consistency and quality. In this …

Clustering ensemble can overcome the instability of clustering and improve clustering
performance. With the rapid development of clustering ensemble, we find that not all …

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 …

Ensemble clustering has emerged as a powerful tool for improving the stability and accuracy
of the clustering task. Although various approaches have been proposed for improving the …

Bagging and boosting are two well-known methods of developing classifier ensembles. It is
generally agreed that the clusterer ensemble methods that utilize the boosting concept can …