On effective presentation of graph patterns: A structural representative approach

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2008, Pages 299-308

  Chen, Chen ^a   Lin, Cindy Xide ^a   Yan, Xifeng ^b   Han, Jiawei ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

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

Author keywords

Frequent graph pattern; Smoothing clustering; Structural representative

Indexed keywords

CARDINALITIES; EMPIRICAL STUDIES; ERROR TOLERANCE; FAST ALGORITHMS; FREQUENT GRAPH PATTERN; FREQUENT PATTERNS; GRAPH DATA; GRAPH PATTERNS; INHERENT NOISE; KNOWLEDGE DISCOVERY; REAL APPLICATIONS; REAL DATA SETS; SIMILAR PATTERN; SMOOTHING-CLUSTERING; STRUCTURAL REPRESENTATIVE; STRUCTURAL REQUIREMENTS;

ALGORITHMS; KNOWLEDGE MANAGEMENT; MINING;

MACROS;

EID: 70349250360     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1458082.1458124     Document Type: Conference Paper

References (23)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB, pages 487-499, 1994.
2. R. J. Bayardo. Efficiently mining long patterns from databases. In SIGMOD Conference, pages 85-93, 1998.
3. C. Borgelt and M. R. Berthold. Mining molecular fragments: Finding relevant substructures of molecules. In ICDM, pages 51-58, 2002.
4. C. Chen, X. Yan, F. Zhu, and J. Han. gapprox: Mining frequent approximate patterns from a massive network. In ICDM, pages 445-450, 2007.
5. M. Deshpande, M. Kuramochi, N. Wale, and G. Karypis. Frequent substructure-based approaches for classifying chemical compounds. IEEE Transactions on Knowledge and Data Engineering, 17(8):1036-1050, 2005.
6. J. Han, J. Wang, Y. Lu, and P. Tzvetkov. Mining top-k frequent closed patterns without minimum support. In ICDM, pages 211-218, 2002.
7. M. Hasan, V. Chaoji, S. Salem, J. Besson, and M. Zaki. Origami: Mining representative orthogonal graph patterns. In ICDM, pages 153-162, 2007.
8. D. S. Hochbaum, editor. Approximation Algorithms for NP-Hard Problems. PWS Publishing, 1997.
9. J. Huan, W. Wang, J. Prins, and J. Yang. Spin: mining maximal frequent subgraphs from graph databases. In KDD, pages 581-586, 2004.
10. L. Kaufman and P. J. Rousseeuw, editors. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley and Sons, 1990.
11. M. Kuramochi and G. Karypis. Frequent subgraph discovery. In ICDM, pages 313-320, 2001.
12. M. Kuramochi and G. Karypis. Finding frequent patterns in a large sparse graph. In SDM, 2004.
13. Y. Liu, J. Li, and H. Gao. Summarizing graph patterns. In ICDE, pages 903-912, 2008.
14. T. Mielikäinen and H. Mannila. The pattern ordering problem. In PKDD, pages 327-338, 2003.
15. R. T. Ng and J. Han. Clarans: A method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering, 14(5):1003-1016, 2002.
16. S. Nijssen and J. N. Kok. A quickstart in frequent structure mining can make a difference. In KDD, pages 647-652, 2004.
17. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In ICDT, pages 398-416, 1999.
18. R. Sharan, S. Suthram, R. M. Kelley, T. Kuhn, S. McCuine, P. Uetz, T. Sittler, R. M. Karp, and T. Ideker. Conserved patterns of protein interaction in multiple species. PNAS, 102(6):1974-1979, 2005.
19. C. Wang and S. Parthasarathy. Summarizing itemset patterns using probabilistic models. In KDD, pages 730-735, 2006.
20. D. Xin, J. Han, X. Yan, and H. Cheng. Mining compressed frequent-pattern sets. In VLDB, pages 709-720, 2005.
21. X. Yan, H. Cheng, J. Han, and D. Xin. Summarizing itemset patterns: a profile-based approach. In KDD, pages 314-323, 2005.
22. X. Yan and J. Han. Closegraph: mining closed frequent graph patterns. In KDD, pages 286-295, 2003.
23. X. Yan, P. S. Yu, and J. Han. Graph indexing: A frequent structure-based approach. In SIGMOD Conference, pages 335-346, 2004.