关键词: , 聚类集成, 一致性学习, 高阶信息, 双随机约束, 结构化, 相似性矩阵

Abstract: Most of the research on clustering ensemble focuses on designing practical consistency learning algorithms. To solve the problems that the quality of base clusters varies and the low-quality base clusters have an impact on the performance of the clustering ensemble， from the perspective of data mining， the intrinsic connections of data were mined based on the base clusters， and a high-order information fusion algorithm was proposed to represent the connections between data from different dimensions， namely Clustering Ensemble with High-order Consensus learning （HCLCE）. Firstly， each high-order information was fused into a new structured consistency matrix. Then， the obtained multiple consistency matrices were fused together. Finally， multiple information was fused into a consistent result. Experimental results show that LCLCE algorithm has the clustering accuracy improved by an average of 7.22%， and the Normalized Mutual Information （NMI） improved by an average of 9.19% compared with the suboptimal Locally Weighted Evidence Accumulation （LWEA） algorithm. It can be seen that the proposed algorithm can obtain better clustering results compared with clustering ensemble algorithms and using one information alone.

Key words: , clustering ensemble, consistency learning, high-order information, double random constraint, structuration, similarity matrix