An Improved Algorithm on Frequent Subgraph Query

Yang Jie Chu; Xin Jia

doi:10.4028/www.scientific.net/AMM.623.169

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 623An Improved Algorithm on Frequent Subgraph Query

An Improved Algorithm on Frequent Subgraph Query

Abstract:

This paper studies the frequent subgraph query issues on graph data set. Combining with the approach that frequent subtree extend to frequent subgraphs proposed by Xian-Tong Li, we propose a new algorithm. This algorithm improved its storage structure avoiding direct subgraph isomorphism judgment, reduced the stability requirements on graph set, and enchanced the overall efficiency of the algorithm.

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Periodical:

Applied Mechanics and Materials (Volume 623)

Pages:

169-173

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.623.169

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Online since:

August 2014

Authors:

Yang Jie Chu, Xin Jia*

Keywords:

Database, Frequent Subgraph, Uncertain Graph

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* - Corresponding Author

References

[1] A. Inokuchi, T. Washio and H. Motoda, An apriori-based algorithm for mining frequent substructures from graph data, LNCS. Journals. 1910 (2000) 13-23.

DOI: 10.1007/3-540-45372-5_2

Google Scholar

[2] M. Kuramochi and G. Karypis, Frequent subgraph discovery, ICDM. (2001) 313-320.

Google Scholar

[3] L. Chen and K. Luo, An imporved method of Apriori algorithm apply to frequent subgraphs mining, Computer Engineering and Applications. 47 (2011) 113-117.

Google Scholar

[4] X. Yan and J. Han, Graph-Based substructure pattern mining, ICDM. (2002) 721-724.

Google Scholar

[5] J. Huan, W. Wang and J. Prins, Efficient mining of frequent subgraphs in the presence of isomorphism, ICDM. (2003) 549-552.

DOI: 10.1109/icdm.2003.1250974

Google Scholar

[6] S. Nijssen and JN. Kok, A quickstart in frequent structure mining can make a difference, International Conference on Knowledge Discovery and Data Mining. (2004) 647-652.

DOI: 10.1145/1014052.1014134

Google Scholar

[7] X. Zou and C. Zheng, Normal encoding based algorithm for frequent subgraphs mining Mini-Micro Systems. 33 (2012) 78-80.

Google Scholar

[8] W. Wang: An efficient algorithm of frequent subgraph mining on uncertain graph databse, Harbin Institute of Technology (2013).

Google Scholar

[9] X. Yan and J. Han, Gspan: Graph-based Substructure Pattern Mining, ICDM. (2003) 548-551.

Google Scholar

[10] Y. Yang, S. Qu and Y. Liu, MRSM- Mining representative maximum frequent subgraph, High Technology Letters. 23 (2013) 337-344.

Google Scholar

[11] X. Li: Research on the technology of graph queries, Harbin Institute of Technology(2009).

Google Scholar

[12] Z. Zeng, J. Wang and J. Zhang, FOGGER: An algorithm for graph generator discovery, Proceedings of the 12th International Conference on Extending Database Technology. 360 (2009) 517-528.

DOI: 10.1145/1516360.1516421

Google Scholar