A voting-based clustering ensemble method is presented. By analyzing the relationship between the clustering structure and accuracy, the highest cohesive cluster member is considered as the benchmark of relabel algorithm to unify cluster labels. Then the voting weights are determined by the distance from cluster centers which data points in different cluster members are divide into. The experimental results show that, the proposed algorithm is greatly improved in accuracy and stability.