Diversity-Based Weighting Schemes for Clustering Ensembles

Clustering ensembles has been recently recognized as an emerging approach to provide more robust solutions to the data clustering problem. Current methods of clustering ensembles typically fall into instance-based, cluster-based, or hybrid approaches; however, most of such methods fail in discriminating among the various clusterings that participate to the ensemble. In this paper, we address the problem of weighting clustering ensembles by proposing general weighting approaches based on different implementations of the notion of diversity. We introduce three weighting schemes for clustering ensembles, called Single Weighting, Group Weighting and Dendrogram Weighting, which are independent of the particular method of clustering ensembles and designed to take into account correlations among the individual clustering solutions in different ways. We show how these schemes can be instantiated into any instance-based, cluster-based and hybrid clustering ensembles methods. Experiments have shown that the performance of the clustering ensembles algorithms increases when the proposed weighting schemes are employed.