Consensus Clustering + Meta Clustering = Multiple Consensus Clustering

Consensus clustering and meta clustering are two important extensions of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings, and meta clustering aims to group similar input clusterings together so that users only need to examine a small number of different clusterings. In this paper, we present a new approach, MCC (stands for multiple consensus clustering), to explore multiple clustering views of a given dataset from the input clusterings by combining consensus clustering and meta clustering. In particular, given a set of input clusterings of a particular data set, MCC employs meta clustering to cluster the input clusterings and then uses consensus clustering to generate a consensus for each cluster of the input clusterings. Extensive experimental results on 11 real world data sets demonstrate the effectiveness of our proposed method.