Many consensus clustering methods ensemble all the basic partition-ings (BPs) with the same weight and without considering their contribution to consensus result. We use the Normalized Mutual Information (NMI) theory to design weight for BPs that participate in the integration, which highlights the contribution of the most diverse BPs. Then an efficient approach K-means is used for consensus clustering, which effectively improves the efficiency of combinatorics learning. Experiment on UCI dataset iris demonstrates the effective of the proposed algorithm in terms of clustering quality.
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