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The combination of multiple clusterings is a difficult problem in the practice of distributed data mining. Both the cluster generation mechanism and the partition integration process influence the quality of the combinations. We propose a data resampling approach for building cluster ensembles that are both robust and stable. In particular, we investigate the effectiveness of a bootstrapping technique in conjunction with several combination algorithms. The empirical study shows that a meaningful consensus partition for an entire set of objects emerges from multiple clusterings of bootstrap samples, given optimal combination algorithm parameters. Experimental results for ensembles with varying numbers of partitions and clusters are reported for simulated and real data sets. Experimental results show improved stability and accuracy for consensus partitions obtained via a bootstrapping technique.