Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …

Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving
both the robustness and the stability of results from individual clustering methods. Weighted …

Due to its ability to combine multiple base clusterings into a probably better and more robust
clustering, the ensemble clustering technique has been attracting increasing attention in …

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 …

As a promising way for heterogeneous data analytics, consensus clustering has attracted
increasing attention in recent decades. Among various excellent solutions, the co …

Although many successful ensemble clustering approaches have been developed in recent
years, there are still two limitations to most of the existing approaches. First, they mostly …

A clustering ensemble provides an elegant framework to learn a consensus result from
multiple prespecified clustering partitions. Though conventional clustering ensemble …

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 …

The objective of consensus clustering is to find a single partitioning which agrees as much
as possible with existing basic partitionings. Consensus clustering emerges as a promising …

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 …