Resampling-based selective clustering ensembles

Experimental results on several real data sets demonstrate that resampling-based selective clustering ensembles method is often able to achieve a better solution when compared with traditional clusteringEnsembles methods.

Abstract

Traditional clustering ensembles methods combine all obtained clustering results at hand. However, we observe that it can often achieve a better clustering solution if only part of all available clustering results are combined. This paper proposes a novel clustering ensembles method, termed as resampling-based selective clustering ensembles method. The proposed selective clustering ensembles method works by evaluating the qualities of all obtained clustering results through resampling technique and selectively choosing part of promising clustering results to build the ensemble committee. The final solution is obtained through combining the clustering results of the ensemble committee. Experimental results on several real data sets demonstrate that resampling-based selective clustering ensembles method is often able to achieve a better solution when compared with traditional clustering ensembles methods.