Scalable mining of large disk-based graph databases

KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2004, Pages 316-325

  Wang, Chen ^a   Wang, Wei ^a   Pei, Jian ^b,c   Zhu, Yongtai ^a   Shi, Baile ^a  

^a FUDAN UNIVERSITY   (China)

^b UNIVERSITY AT BUFFALO   (United States)

^c SIMON FRASER UNIVERSITY   (Canada)

Author keywords

Frequent graph pattern; Graph mining; Index, graph database

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; GRAPH THEORY; INDEXING (OF INFORMATION); PROBLEM SOLVING; SEMANTICS; WORLD WIDE WEB; XML;

FREQUENT GRAPH PATTERNS; GRAPH DATABASE; GRAPH MINING; INDEX STRUCTURES;

DATA MINING;

EID: 12244251830     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1014052.1014088     Document Type: Conference Paper

References (12)

1. D.M. Bayada, R. W. Simpson, and A. P. Johnson. An algorithm for the multiple common subgraph problem. J, of Chemical Information & Computer Sci., 32:680-685, 1992.
2. C. Borgelt and M.R. Berthold. Mining molecular fragments: Finding relevant substructures of molecules. In Proc. 8002 Int. Conf. Data Mining (ICDM'02), Maebashi TERRSA, Maebashi City, Japan, Dec. 2002.
3. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2002.
4. L. B. Holder, D. J. Cook, and S. Djoko. Substructure discovery in the subdue system. In Proc. AAAI'94 Workshop Knowledge Discovery in Databases (KDD'94), pages 359-370, Seattle, WA, July 1994.
5. A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In Proc. 2000 European Symp. Principle of Data Mining and Knowledge Discovery (PKDD'00), pages 13-23, Lyon, France, Sept. 2000.
6. M. Kuramochi and G. Karypis. Frequent subgraph discovery. In Proc. 2001 Int. Conf. Data Mining (ICDM'01), pages 313-320, San Jose, CA, Nov. 2001.
7. Y. Takahashi, Y. Satoh, and S. Sasaki. Recognition of largest common fragment among a variety of chemical structures. Analytical Sciences, 3:23-38, 1987.
8. N. Vanetik, E. Gudes, and S.E. Shimony. Computing frequent graph patterns from semistructured data. In Proc. 2002 Int. Conf. Data Mining (ICDM'02), Maebashi TERRSA, Maebashi City, Japan, Dec. 2002.
9. X. Yan and J. Han. Closegraph: Mining closed frequent graph patterns. In Proceedings of the 9th A CM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'03), Washington, D.C, 2003.
10. X. Yan, P.S. Yu, and J. Han. Graph indexing: A frequent structure-based approach. In Proc. 2004 ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'04), Paris, France, June 2004.
11. Y. Yan and J. Han. gspan: Graph-based substructure pattern mining. In Proc. 2002 Int. Conf. on Data Mining (ICDM'02), Maebashi, Japan, December 2002.
12. M.J. Zaki. Efficiently mining frequent trees in a forest. In Proc. 2002 Int. Conf. on Knowledge Discovery and Data Mining (KDD'02), Edmonton, Alberta, Canada, July 2002.