Weighted-object ensemble clustering: methods and analysis

Abstract

Ensemble clustering has attracted increasing attention in recent years. Its goal is to combine multiple base clusterings into a single consensus clustering of increased quality. Most of the existing ensemble clustering methods treat each base clustering and each object as equally important, while some approaches make use of weights associated with clusters, or to clusterings, when assembling the different base clusterings. Boosting algorithms developed for classification have led to the idea of considering weighted objects during the clustering process. However, not much effort has been put toward incorporating weighted objects into the consensus process. To fill this gap, in this paper, we propose a framework called Weighted-Object Ensemble Clustering (WOEC). We first estimate how difficult it is to cluster an object by constructing the co-association matrix that summarizes the base clustering results, and we then embed the corresponding information as weights associated with objects. We propose three different consensus techniques to leverage the weighted objects. All three reduce the ensemble clustering problem to a graph partitioning one. We experimentally demonstrate the gain in performance that our WOEC methodology achieves with respect to state-of-the-art ensemble clustering methods, as well as its stability and robustness.