A Fast Frequent Subgraph Mining Algorithm

被引：0

作者：

Wu, Jia ^{[1
]}

Chen, Ling ^{[1
]}

机构：

[1] Yangzhou Univ, Dept Comp Sci, Yangzhou 225009, Peoples R China

来源：

PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE FOR YOUNG COMPUTER SCIENTISTS, VOLS 1-5 | 2008年

关键词：

Graph; associated matrix; isomorphism; data mining;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

An algorithm for mining frequent subgraphs in large database of labeled graphs is proposed. The algorithm uses incidence matrix to represent the labeled graphs and to detect their isomorphism. Starting from the frequent edges from the graph database, the algorithm searches the frequent subgraphs by adding frequent edges progressively. By normalizing the incidence matrix of the graph, the algorithm can effectively; reduce the computational cost on verifying the isomorphism of the subgraphs. Experimental results show that the algorithm has higher speed and efficiency than that of other similar ones.

引用

页码：82 / 87

页数：6

共 26 条

[1] Berendt B, 2002, LECT NOTES COMPUT SC, V2342, P264
[2] Cook D., 2007, MINING GRAPH DATA, P99
[3] Dehaspe L., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining, P30
[4] Frequent sub-structure-based approaches for classifying chemical compounds
Deshpande, M
Kuramochi, M
Karypis, G
[J]. THIRD IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2003, : 35 - 42
[5] Faust K., 1994, SOCIAL NETWORK ANAL, V249
[6] GONZALEZ J, 2001, P PRED TOX CHALL WOR
[7] Holder L. B., 1994, P AAAI WORKSH KNOWL, P169
[8] HUAN J, 2003, P ICDM
[9] INOKUCHI A, 2000, P PAKDD
[10] JENSEN D, 1998, ART INT LINK AN PAP

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