Exploiting virtual patterns for automatically pruning the search space

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

Volumn 3933 LNCS, Issue , 2006, Pages 202-221

  Soulet, Arnaud ^a   Crémilleux, Bruno ^a  

^a Centre National de la Recherche Scientifique   (France)

Author keywords

Constraint based mining; Pruning conditions; Virtual patterns

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; DATABASE SYSTEMS; PATTERN RECOGNITION; VIRTUAL REALITY;

CONSTRAINT-BASED MINING; GENERALIZATION; PRUNING CONDITIONS; VIRTUAL PATTERNS;

DATA MINING;

EID: 33745802367     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11733492_12     Document Type: Conference Paper

References (25)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In J. B. Bocca, M. Jarke, and C. Zaniolo, editors, VLDB, pages 487-499. Morgan Kaufmann, 1994.
2. R. Agrawal and R. Srikant. Mining sequential patterns. In P. S. Yu and A. L. P. Chen, editors, ICDE, pages 3-14. IEEE Computer Society, 1995.
3. R. J. Bayardo. The hows, whys, and whens of constraints in itemset and rule discovery. In Proceedings of the Workshop on Inductive Databases and Constraint Based Mining, 2005.
4. J. Besson, R. Pensa, C. Robardet, and J.-F. Boulicaut. Constraint-based mining of fault-tolerant patterns from boolean data . In 4th International Workshop on Knowledge Discovery in Inductive Databases (KDID'05) co-located with the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases PKDD'05 , pages 13-26, Porto, Portugal, 10 2005.
5. K. S. Beyer and R. Ramakrishnan. Bottom-up computation of sparse and iceberg cubes. In A. Delis, C. Faloutsos, and S. Ghandeharizadeh, editors, SIGMOD Conference, pages 359-370. ACM Press, 1999.
6. F. Bonchi, F. Giannotti, A. Mazzanti, and D. Pedreschi. Exante: Anticipated data reduction in constrained pattern mining. In N. Lavrac, D. Gamberger, H. Blockeel, and L. Todorovski, editors, PKDD, volume 2838 of Lecture Notes in Computer Science, pages 59-70. Springer, 2003.
7. F. Bonchi and C. Lucchese. On closed constrained frequent pattern mining. In ICDM, pages 35-42. IEEE Computer Society, 2004.
8. F. Bonchi and C. Lucchese. Pushing tougher constraints in frequent pattern mining. In Ho et al. [14], pages 114-124.
9. C. Bucila, J. Gehrke, D. Kifer, and W. M. White. Dualminer: a dual-pruning algorithm for itemsets with constraints. In KDD, pages 42-51. ACM, 2002.
10. G. Dong and J. Li. Efficient mining of emerging patterns: discovering trends and differences. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'99), pages 43-52, New York, NY, USA, 1999. ACM Press.
11. K. Gade, J. Wang, and G. Karypis. Efficient closed pattern mining in the presence of tough block constraints. In W. Kim, R. Kohavi, J. Gehrke, and W. DuMouchel, editors, KDD, pages 138-147. ACM, 2004.
12. F. Geerts, B. Goethals, and T. Mielikäinen. Tiling databases. In E. Suzuki and S. Arikawa, editors, Discovery Science, volume 3245 of Lecture Notes in Computer Science, pages 278-289. Springer, 2004.
13. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In W. Chen, J. F. Naughton, and P. A. Bernstein, editors, SIGMOD Conference, pages 1-12. ACM, 2000.
14. T. B. Ho, D. Cheung, and H. Liu, editors. Advances in Knowledge Discovery and Data Mining, 9th Pacific-Asia Conference, PAKDD 2005, Hanoi, Vietnam, May 18-20, 2005, Proceedings, volume 3518 of Lecture Notes in Computer Science. Springer, 2005.
15. T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Commun. ACM, 39(11):58-64, 1996.
16. D. Kifer, J. Gehrke, C. Bucila, and W. M. White. How to quickly find a witness. In PODS, pages 272-283. ACM, 2003.
17. M. Kuramochi and G. Karypis. Frequent subgraph discovery. In N. Cercone, T. Y. Lin, and X. Wu, editors, ICDM, pages 313-320. IEEE Computer Society, 2001.
18. S. D. Lee and L. D. Raedt. An algebra for inductive query evaluation. In Proceedings of the Second International Workshop on Inductive Databases (KDID'03), pages 80-96. Rudjer Boskovic Institute, Zagreb, Croatia, September 2003.
19. H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Min. Knowl. Discov., 1(3):241-258, 1997.
20. T. M. Mitchell. Generalization as search. Artif. Intell., 18(2):203-226, 1982.
21. R. T. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained association rules. In L. M. Haas and A. Tiwary, editors, SIGMOD Conference, pages 13-24. ACM Press, 1998.
22. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In C. Beeri and P. Buneman, editors, ICDT, volume 1540 of Lecture Notes in Computer Science, pages 398-416. Springer, 1999.
23. J. Pei, J. Han, and L. V. S. Lakshmanan. Mining frequent item sets with convertible constraints. In ICDE, pages 433-442. IEEE Computer Society, 2001.
24. A. Soulet and B. Crémilleux. An efficient framework for mining flexible constraints. In Ho et al. [14], pages 661-671.
25. A. Soulet and B. Crémilleux. Optimizing constraint-based mining by automatically relaxing constraints. In Proceedings of The Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.