Frequent hypergraph mining

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

Volumn 4455 LNAI, Issue , 2007, Pages 244-259

  Horváth, Tamás ^a   Bringmann, Björn ^b   De Raedt, Luc ^b  

^a FRAUNHOFER IAIS   (Germany)

^b UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATION THEORY; GRAPH THEORY; POLYNOMIALS; PROBLEM SOLVING;

CITATION ANALYSIS; FREQUENT HYPERGRAPH MINING;

DATA MINING;

EID: 38049103260     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-73847-3_26     Document Type: Conference Paper

References (21)

1. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, pp. 307-328. AAAI/MIT Press (1996)
2. Berge, C.: Hypergraphs. North Holland Mathematical Library, vol. 445. Elsevier, Amsterdam (1989)
3. Boros, E., Elbassioni, K., Gurvich, V., Khachiyan, L., Makino, K.: Dual bounded hypergraphs: A survey. In: Proc. of the 2nd SIAM Conference on Data Mining, pp. 87-98 (2002)
4. Chi, Y., Nijssen, S., Muntz, R.R., Kok, J.N.: Frequent subtree mining-an overview. Fundamenta Informaticae 66, 161-198 (2005)
5. Dehaspe, L., Toivonen, H.: Discovery of frequent DATALOG patterns. Data Mining and Knowledge Discovery 3, 7-36 (1999)
6. Diestel, R.: Graph theory, 3rd edn. Springer, Heidelberg (2005)
7. Džeroski, S., Lavrač, N. (eds.): Relational Data Mining. Springer, New York (2002)
8. Ebbinghaus, H.-D., Flum, J.: Finite Model Theory, 2nd edn. Springer, Heidelberg (1999)
9. Eiter, T., Gottlob, G.: Identifying the minimal transversals of a hypergraph and related problems. SIAM Journal on Computing 24(6), 1278-1304 (1995)
10. Fagin, R.: Degrees of acyclicity for hypergraphs and relational database schemes. Journal of the ACM 30(3), 514-550 (1983)
11. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to NPCompleteness. Freeman, San Francisco, CA (1979)
12. Gunopulos, D., Khardon, R., Mannila, H., Saluja, S., Toivonen, H., Sharm, R.S.: Discovering all most specific sentences. ACM Transactions on Database Systems 28(2), 140-174 (2003)
13. Horváth, T., Ramon, J., Wrobel, S.: Frequent subgraph mining in outerplanar graphs. In: Proc. of the 12th ACM SIGKDD International Conference on Knowledge discovery and Data Mining, pp. 197-206. ACM Press, New York (2006)
14. Johnson, D.S., Yannakakis, M., Papadimitriou, C.H.: On generating all maximal independent sets. Information Processing Letters 27(3), 119-123 (1988)
15. Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241-258 (1997)
16. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259-289 (1997)
17. Nijssen, S., Kok, J.N.: Efficient frequent query discovery in FARMER. In: Proc. of the 17th International Joint Conference on Artificial Intelligence, pp. 891-896. Morgan Kaufmann, San Francisco (2001)
18. Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 647-652. ACM Press, New York (2004)
19. Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining, pp. 721-724. IEEE Computer Society Press, Los Alamitos (2002)
20. Yu, CT., Ozsoyoglu, M.Z.: An algorithm for tree-query membership of a distributed query. In: Proceedings of Computer Software and Applications Conference, pp. 306-312. IEEE Computer Society Press, Los Alamitos (1979)
21. Zaki, M.J.: Efficiently mining frequent trees in a forest. In: Proceedings of the 8th ACM SIGKDD International Conference, pp. 71-80. ACM Press, New York (2002)