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 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 …

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 …

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 …

The advent of architectures such as the Internet of Things (IoT) has led to the dramatic
growth of data and the production of big data. Managing this often-unlabeled data is a big …

A new criterion for clusters validation is proposed in the paper and based on the new cluster
validation criterion a clustering ensemble framework is proposed. The main idea behind the …

In order to improve the performance of a clustering on a data set, a number of primary
partitions are generated and stored in an ensemble and their aggregated consensus …

A clustering ensemble combines in a consensus function the partitions generated by a set of
independent base clusterers. In this study both the employment of particle swarm clustering …

Cluster ensemble is a very important tool that aggregates several base clusterings to
generate a single output clustering with improved robustness and stability. However, the …

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 …