An efficient clustering ensemble selection algorithm. (2015) - Liu, Limin; Liao, Zhifang; Liao, Zhining
Clustering ensemble selection has been confirmed that it can always achieve better result than traditional clustering ensemble algorithms. However, many selective clustering ensemble algorithms cannot eliminate the inferior quality partitions' influence and the accuracy of clustering is not high. In order to solve the problems, the paper proposes a new selective clustering ensemble algorithm. The algorithm, firstly, uses clustering validity evaluation to evaluate all available clustering ensemble partitions and selects the best quality as reference partition; secondly, the paper defines selection strategy via the quality and diversity; lastly, the paper proposes the adaptive weight strategy of ensemble members. The experimental results show that the new algorithm is effective and clustering performance could be significantly improved.