Consensus clustering by graph based approach

In this paper, we propose G-Cons, an extension of a graph minimal coloring paradigm for consensus clustering. Based on the coassociation values between data, our approach is a graph partitioning one which yields a combined partition by maximizing an objective function given by the average mutual information between the consensus partition and all initial combined clusterings. It exhibits more important consensus clustering features (quality and computational complexity) and enables to build a combined partition by improving the stability and accuracy of clustering solutions. The proposed approach is evaluated against benchmark databases and promising results are obtained compared to other consensus clustering techniques.