Consensus clustering, i.e. the task of combining the outcomes of several clustering systems into a single partition, has lately attracted the attention of researchers in the unsupervised classification field, as it allows the creation of clustering committees that can be applied with multiple interesting purposes, such as knowledge reuse or distributed clustering. However, little attention has ...

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 i...

The clustering ensembles contains multiple partitions are divided by different clustering algorithms into a single clustering solutions. Clustering ensembles used for improving robustness, stability, and accuracy of unsupervised classification solutions. The major problem of clustering ensemble is the consensus function. Consensus functions in clustering ensembles including hyperactive graph pa...

Given a collection C of partitions of a base set S, the NP-hard Consensus Clustering problem asks for a partition of S which has a total Mirkin distance of at most t to the partitions in C, where t is a nonnegative integer. We present a parameterized algorithm for Consensus Clustering with running time O(4.24 ·k+ |C| · |S|), where k := t/|C| is the average Mirkin distance of the solution partit...