Abstract Aiming at the problems of low efficiency and accuracy of data mining, a graph mining model using Markov clustering based on annular network motifs is proposed. Firstly, the Erdo″s-Rényi model is employed to generate random graphs according to the vertices set of the input graph. Annular sub-graphs are judged by the additive property of vectors in the process of sub-graph mining from input and random graphs. In the next step, the motif statistical characteristics are calculated and used to label the annular motifs. Then, the correlation matrix of absolute contribution of edges is solved in the graph, and the threshold is obtained by dynamic threshold method for the binarization of matrix. Finally, inflation and expansion processes are carried out on the sparsified graph data to achieve the state of convergence. Experimental results show that the proposed model can effectively reduce the running time and improve the mining efficiency of the graph with the guaranteed clustering quality.
About author:: (REN Yonggong(Corresponding author), born in 1972, Ph.D., professor. His research interests include database technology, data mining and intelligent information calculation.) (SUO Quanming, born in 1986, master student. His research interests include data mining.) (LIU Yang, born in 1986, Ph.D., lectu-rer. His research interests include digital image processing and radar retrieval.)
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