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 …

Cluster ensembles generate a large number of different clustering solutions and combine
them into a more robust and accurate consensus clustering. On forming the ensembles, the …

Cluster ensemble has proved to be a good alternative when facing cluster analysis
problems. It consists of generating a set of clusterings from the same dataset and combining …

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 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 ensembles combine multiple partitions of data into a single clustering solution of
better quality. Inspired by the success of supervised bagging and boosting algorithms, we …

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 …

The objective of clustering ensemble is to find the underlying structure of data based on a
set of clustering results. It has been observed that the samples can change between clusters …

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 …

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 …