关键词: 熵, 多视角聚类, 划分, 权值, 集成策略, UCI数据集

Abstract: Currently, the maximum entropy clustering (MEC) merges the multi-view samples to process the multi-view clustering task. However, this will damage the independence of each view, and affect the final partition results. Aiming at this problem, this paper proposes a multi-view collaborative partition maximum entropy clustering (CoMEC) algorithm, which joins a constraint to coordinate each perspective space partition, to make each view in a separate clustering process consider the influence of other views. Then this paper proposes the enhanced weighted view version called W-CoMEC by identifying the importance of each view. Finally this paper applies the geometric average integration strategy to obtain the global partition results. The experimental results on a synthetic multi-view dataset and several UCI real-world multi-view datasets show that the proposed algorithm outperforms or is at least comparable to the     existing clustering technology in dealing with multi-view clustering task.

Key words: entropy, multi-view clustering, partition, weight, integration strategy, UCI dataset