Efficient mining of frequent subgraphs in the presence of isomorphism

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2003, Pages 549-552

  Huan, Jun ^a   Wang, Wei ^a   Prins, Jan ^a  

^a Brooks Computer Science Building   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 78149328300     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. C. Borgelt and M. R. Berhold. Mining molecular fragments: Finding relevant substructures of molecules. In ICDM'02.
2. R. Goldman and J. Widom. Dataguides: Enabling query formulation and optimization in semistructured databases. In VLDB'97.
3. J. Huan, W.Wang, and J. Prins. Efficient mining of frequent subgraph in the presence of isomorphism. UNC computer science technique report TR03-021, 2003.
4. A. Inokuchi, T.Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In PKDD'00.
5. M. Kuramochi and G. Karypis. Frequent subgraph discovery. In ICDM'01.
6. K. Shearer, H. Bunks, and S. Venkatesh. Video indexing and similarity retrieval by largest common subgraph detection using decision trees. Pattern Recognition, 34(5):1075-91, 2001.
7. A. Srinivasan, R. D. King, S. H. Muggleton, and M. Sternberg. The predictive toxicology evaluation challenge. In Proc. of the 15th International Joint Conferrence on Artificial Intelligence (IJCAI), pages 1-6, 1997.
8. N. Vanetik, E. Gudes, and E. Shimony. Computing frequent graph patterns from semi-structured data. ICDM'02, 2002.
9. X. Yan and J. Han. gspan: Graph-based substructure pattern mining. In ICDM'02.
10. M. J. Zaki. Efficiently mining frequent trees in a forest. In SIGKDD'02.
11. M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New algorithms for fast discovery of association rules. In SIGKDD'97.