Abstract: According to different data characteristics,clustering analysis adopts different similarity measures.However,the data distribution is complex in the real world,and there are various phenomena such as irregular distribution and uneven density.Considering attribute similarity or distribution structure connectivity alone will reduce clustering performance.Therefore,this paper proposes a clustering algorithm based on attribute similarity and distributed structure connectivity(ASDSC).Firstly,a completely undirected graph is constructed using all data instances,and a novel similarity measurement method is defined to calculate the node similarity by the topology structure and the attributes similarity,and the adjacency matrix are constructed to update the weights of edges.Secondly,based on the adjacency matrix,random walk with increasing step is performed.Subsequently,the cluster centers and their numbers are obtained according to the connected centrality of nodes,and the connectivity of other nodes is also acquired.Then,the connectivity is used to calculate the dependencies among nodes,and the propagation process of cluster number is carried out accordingly until the clustering process is completed.Finally,comparative experiments with 5 advanced clustering algorithms are conducted on 16 synthetic datasets and 10 real datasets,and the result show that the ASDSC algorithm has achieved excellent performance.

Key words: Clustering, Similarity measure, Attribute similarity, Distributed structure connectivity, Cluster number propagation