Abstract: Affinity Propagation (AP) has advantages on efficiency and accuracy,and has no need to set the number of clusters,but is not suitable for large-scale data clustering．Hierarchical Affinity Propagation (HAP) was proposed to overcome this problem．Firstly,the data set was divided into several subsets that can be effectively clustered by AP to select the exemplars of each subset．Then,AP clustering was implemented again on all the subset exemplars to select exemplars of the whole data set．Finally,all the data points were clustered according to similarities with the exemplars,and realizing efficient clustering of large-scale data set．The experimental results on real and simulated data sets show that,compared with traditional AP and adaptive AP,HAP reduces the time consumption greatly and achieves a good clustering result in the meanwhile.

Key words: Data clustering,Affinity propagation,Hierarchical selecting,Clustering center