Local query mining in a probabilistic prolog

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2009, Pages 1095-1100

  Kimmig, Angelika ^a   De Raedt, Luc ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; INDUCTIVE LOGIC PROGRAMMING (ILP); QUERY PROCESSING;

A-PROLOG; BIOLOGICAL NETWORKS; INTERESTINGNESS; LOCAL PATTERNS; MINING TASKS; PROLOG SYSTEMS; QUERY MINING; STATISTICAL RELATIONAL LEARNING;

PROLOG (PROGRAMMING LANGUAGE);

EID: 77956218695     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. Inkeri Verkamo. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, pages 307-328. AAAI/MIT Press, 1996.
2. C. Kit Chui, B. Kao, and E. Hung. Mining frequent itemsets from uncertain data. In PAKDD, pages 47-58, 2007.
3. N. N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. In VLDB, pages 864-875, 2004.
4. L. De Raedt and J. Ramon. Condensed representations for inductive logic programming. In KR, pages 438-446, 2004.
5. L. De Raedt, A. Kimmig, and H. Toivonen. ProbLog: A probabilistic Prolog and its application in link discovery. In IJCAI, pages 2462-2467, 2007.
6. L. Dehaspe and H. Toivonen. Discovery of frequent datalog patterns. Data Min. Knowl. Discov., 3(1):7-36, 1999.
7. L. Dehaspe, H. Toivonen, and R. D. King. Finding frequent substructures in chemical compounds. In KDD, pages 30-36, 1998.
8. F. Esposito, N. Fanizzi, S. Ferilli, and G. Semeraro. Ideal theory refinement under object identity. In ICML, pages 263-270, 2000.
9. N. Fuhr. Probabilistic Datalog: Implementing logical information retrieval for advanced applications. JASIS, 51(2):95-110, 2000.
10. A. Kimmig, L. De Raedt, and H. Toivonen. Probabilistic explanation based learning. In ECML, pages 176-187, 2007.
11. H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Min. Knowl. Discov., 1(3):241-258, 1997.
12. S. Morishita and J. Sese. Traversing itemset lattice with statistical metric pruning. In PODS, pages 226-236, 2000.
13. R. T. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained association rules. In SIGMOD, pages 13-24, 1998.
14. T. Sato and Y. Kameya. Parameter learning of logic programs for symbolic-statistical modeling. J. Artif. Intell. Res. (JAIR), 15:391-454, 2001.
15. P. Sevon, L. Eronen, P. Hintsanen, K. Kulovesi, and H. Toivonen. Link discovery in graphs derived from biological databases. In DILS, pages 35-49, 2006.
16. S. Tsur, J. D. Ullman, S. Abiteboul, C. Clifton, R. Motwani, S. Nestorov, and A. Rosenthal. Query flocks: A generalization of association-rule mining. In SIGMOD, pages 1-12, 1998.
17. Q. Zhang, F. Li, and K. Yi. Finding frequent items in probabilistic data. In SIGMOD, pages 819-832, 2008.