A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble

Clustering ensembles can obtain more superior final results by combining multiple different clustering results. The qualities of the points, clusters, and partitions play crucial roles in the consistency of the clustering process. However, existing methods mostly focus on one or two aspects of them, without a comprehensive consideration of the three aspects. This paper proposes a three-level weighted clustering ensemble algorithm namely unified point-cluser-partition algorithm (PCPA). The first step of the PCPA is to generate the adjacency matrix by base clusterings. Then, the central step is to obtain the weighted adjacency matrix by successively weighting three layers, i.e., points, clusters, and partitions. Finally, the consensus clustering is obtained by the average link method. Three performance indexes, namely F, NMI, and ARI, are used to evaluate the accuracy of the proposed method. The experimental results show that: Firstly, as expected, the proposed three-layer weighted clustering ensemble can improve the accuracy of each evaluation index by an average value of 22.07% compared with the direct clustering ensemble without weighting; Secondly, compared with seven other methods, PCPA can achieve better clustering results and the proportion that PCPA ranks first is 28/33.