A self-supervised framework for clustering ensemble

Clustering ensemble refers to combine a number of base clusterings for a particular data set into a consensus clustering solution. In this paper, we propose a novel self-supervised learning framework for clustering ensemble. Specifically, we treat the base clusterings as pseudo class labels and learn classifiers for each of them. By adding priors to the parameters of these classifiers, we capture the relationships between different base clusterings and meanwhile obtain a a single consolidated clustering result. In the proposed framework, we are able to incorporate the original data features to improve the performance of clustering ensemble. Another advantage, which distinguishes the proposed framework from the traditional clustering ensemble approaches, is with the generalization capability, i.e. it is able to assign the incoming data instances to the consensus clusters directly based on the original data features. We conduct extensive experiments on multiple real world data sets to show the effectiveness of our method. © 2013 Springer-Verlag Berlin Heidelberg.Clustering ensemble refers to combine a number of base clusterings for a particular data set into a consensus clustering solution. In this paper, we propose a novel self-supervised learning framework for clustering ensemble. Specifically, we treat the base clusterings as pseudo class labels and learn classifiers for each of them. By adding priors to the parameters of these classifiers, we capture the relationships between different base clusterings and meanwhile obtain a a single consolidated clustering result. In the proposed framework, we are able to incorporate the original data features to improve the performance of clustering ensemble. Another advantage, which distinguishes the proposed framework from the traditional clustering ensemble approaches, is with the generalization capability, i.e. it is able to assign the incoming data instances to the consensus clusters directly based on the original data features. We conduct extensive experiments on multiple real world data sets to show the effectiveness of our method. © 2013 Springer-Verlag Berlin Heidelberg