gPrune: A constraint pushing framework for graph pattern mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4426 LNAI, Issue , 2007, Pages 388-400

  Zhu, Feida ^a   Yan, Xifeng ^a   Han, Jiawei ^a   Yu, Philip S ^b  

^a UIUC ^*

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINT THEORY; DATA REDUCTION; DATA STRUCTURES; PATTERN RECOGNITION;

ANTIMONOTONICITIES; PRUNING PROPERTIES;

GRAPH THEORY;

EID: 38049184755     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71701-0_38     Document Type: Conference Paper

References (30)

1. Boulicaut,J.,De Raedt, L.: Inductive Databases and Constraint-Based Mining. (ECML'02) Tutorial.
2. Koyuturk, M., Grama, A., Szpankowski, W.: An efficient algorithm for detecting frequent subgraphs in biological networks. (ISMB'04). 200-207
3. Borgelt, C., Berthold, M.R.: Mining molecular fragments: Finding relevant substructures of molecules. (ICDM'02), 211-218
4. Deshpande, M., Kuramochi, M., Karypis, G.: Frequent sub-structure-based approaches for classifying chemical compounds. (ICDM'03). 35-42
5. Huan, J., Wang, W., Bandyopadhyay, D., Snoeyink, J., Prins, J., Tropsha, A.: Mining spatial motifs from protein structure graphs. (RECOMB '04), 308-315
6. Deshpande, M., Kuramochi, M., Wale, N., Karypis, G.: Frequent substructure-based approaches for classifying chemical compounds. IEEE TKDE 17(8) (2005) 1036-1050
7. Yan, X., Yu, P.S., Han, J.: Graph indexing: A frequent structure-based approach.(SIGMOD'04), 335-346
8. Butte, A., Tamayo, P., Slonim, D., Golub, T., Kohane, I.: Discovering functional relationships between ma expression and chemotherapeutic susceptibility. In: Proc. of the National Academy of Science. Volume 97. (2000) 12182-12186
9. Ng, R., Lakshmanan, L.V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained associations rules. (SIGMOD'98), 13-24
10. Bucila, C., Gehrke, J., Kifer, D., White, W.: DualMiner: A dual-pruning algorithm for item-sets with constraints. Data Mining and Knowledge Discovery 7 (2003) 241-272
11. Bonchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Exante: Anticipated data reduction in constrained pattern mining. (PKDD'03)
12. Bonchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Exante: A preprocessing method for frequent-pattern mining. In: IEEE Intelligent Systems 20(3). (2005) 25-31
13. Bonchi, F., Lucchese, C.: Pushing tougher constraints in frequent pattern mining. (PAKDD'04), 114-124
14. Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. (PKDD'00), 13-23
15. Kuramochi, M., Karypis, G.: Frequent subgraph discovery. (ICDM'01), 313-320
16. Vanetik, N., Gudes, E., Shimony, S.E.: Computing frequent graph patterns from semistructured data. (ICDM'02), 458-465
17. Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. (ICDM'02), 721-724
18. Huan, J., Wang, W., Prins, J.: Efficient mining of frequent subgraph in the presence of isomorphism. (ICDM'03), 549-552
19. Prins, J., Yang, J., Huan, J., Wang, W.: Spin: Mining maximal frequent subgraphs from graph databases. (KDD'04), 581-586
20. Nijssen, S., Kok, J.: A quickstart in frequent structure mining can make a difference. (KDD'04), 647-652
21. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. (VLDB'94), 487-499
22. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. (SIGMOD'00), 1-12
23. Yan, X., Zhou, X.J., Han, J.: Mining closed relational graphs with connectivity constraints. (KDD'05), 324-333
24. Goldberg, A.: Finding a maximum density subgraph. Berkeley Tech Report, CSD-84-171
25. Seno, M., Karypis, G.: Slpminer: An algorithm for finding frequent sequential patterns using length decreasing support constraint. (ICDM'02), 418-425
26. Dong, G., Han, J., Lam, J., Pei, J., Wang, K., Zou, W.: Mining constrained gradients in multi-dimensional databases. IEEE TKDE 16 (2004) 922-938
27. Gade, K., Wang, J., Karypis, G.: Efficient closed pattern mining in the presence of tough block constraints. (KDD'04), 138-147
28. Zaki, M.: Generating non-redundant association rules.(KDD'00), 34-43
29. Wang, C., Zhu, Y., Wu, T., Wang, W., Shi, B.: Constraint-based graph mining in large database. (In: APWeb 2005) 133-144
30. Yan, X., Han, J.: CloseGraph: Mining Closed Frequent Graph Patterns (KDD'03)286-295