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Uncertain maximal frequent subgraph mining algorithm based on adjacency matrix and weight

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Abstract

How to mine many interesting subgraphs in uncertain graph has become an important research field in data mining. In this paper, a novel algorithm Uncertain Maximal Frequent Subgraph Mining Algorithm Based on Adjacency Matrix and Weight (UMFGAMW) is proposed. The definition of the adjacency matrix and the standard matrix coding for uncertain graph are presented. The correspondence between the adjacency matrix and uncertain graph is established. A new vertex ordering policy for computing the standard coding of uncertain graph adjacency matrix is designed. The complexity of uncertain graph standard coding is reduced, and the matching speed of uncertain subgraph standard coding is improved. The definition of the weight of uncertain graph and the mean weight of uncertain edge is proposed. The importance of the uncertain subgraphs that meet the minimum support threshold in the graph dataset is fully considered. Finally, a depth-first search weighted uncertain maximal frequent subgraph mining algorithm is discussed. According to the limiting condition of the uncertain maximum frequent subgraph and weighed uncertain edge, the number of mining results is reduced effectively. Experimental results demonstrate that the UMFGAMW algorithm has higher efficiency and better scalability.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.61170190), the Nature Science Foundation of Hebei Province (No.F2015402114, F2015402070, F2015402119) and Foundation of Hebei Educational Committee (No.YQ2014014, QN20131081). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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Authors and Affiliations

  1. Hebei University of Engineering, Handan, Hebei, China

    Di Wu & Long Sheng

  2. Yanshan University, Qinhuangdao, Hebei, China

    Jiadong Ren

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  1. Di Wu
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  2. Jiadong Ren
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  3. Long Sheng
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Correspondence to Di Wu.

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Wu, D., Ren, J. & Sheng, L. Uncertain maximal frequent subgraph mining algorithm based on adjacency matrix and weight. Int. J. Mach. Learn. & Cyber. 9, 1445–1455 (2018). https://doi.org/10.1007/s13042-017-0655-y

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  • DOI: https://doi.org/10.1007/s13042-017-0655-y

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