Mining confident minimal rules with fixed-consequents

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn , Issue , 2004, Pages 6-13

  Rahal, Imad ^a   Ren, Dongmei ^a   Wu, Weihua ^a   Perrizo, William ^a  

^a North Dakota State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION RULE MINING (ARM); CONFIDENT RULES; MARKET BASKET RESEARCH (MBR); PRUNING;

ALGORITHMS; DATA REDUCTION; DATABASE SYSTEMS;

DATA MINING;

EID: 16244396708     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2004.85     Document Type: Conference Paper

References (20)

1. R. Agrawal, T. Imielinski, and A. Swami, Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD (Washington, D.C.), 1993.
2. R. Agrawal and R. Srikant, Fast algorithms for mining association rules. Proceeding of the VLDB (Santiago, Chile), 1994.
3. K. Ahmed, N. El-Makky and Y. Taha, "A note on 'Beyond Market Baskets: Generalizing association rules to correlations'". ACM SIGKDD Explorations, Vol. 1, Issue 2, pp. 46-48, 2000.
4. Y. Bastide, N. Pasquier, R. Taouil, G. Stumme, and L. Lakhal, Mining Minimal Non-Redundant Association Rules using Frequent Closed Item sets. Proceedings of the First International Conference on Computational Logic (London, UK), 2000.
5. R. Bayardo, R. Agrawal, and D. Gunopulos, Constraint-Based Rule Mining in Large, Dense Databases. Proceedings of the IEEE ICDE (Sydney, Australia), 188-197, 1999.
6. R. Bayardo and R. Agrawal, Mining the most interesting rules. Proceedings of the ACM SIGKDD (San Diego, CA), 1999.
7. C. Becquet, S. Blachon, B. Jeudy, JF. Boulicaut, and O. Grandrillon, "Strong-association-rule mining for large-scale gene expression data analysis: a case on human SAGE data". Genome Biology, 3(12), 2002.
8. F. Coene, P. Leng, and S. Ahmed, "Data Structure for Association Rule Mining: T-Tree and P-Trees." IEEE transactions on Knowledge and Data Engineering 16(6):774-778, 2004.
9. E. Cohen, M. Datar, S. Fujiwara, A. Gionis, P. Indyk, R. Motwani, J. D. Ullman, and C. Yang, "Finding interesting associations without support pruning". IEEE Transactions on Knowledge and Data Engineering, 13(1):64-78, 2001.
10. Q. Ding, M. Khan, A. Roy, and W. Perrizo, The p-tree algebra. Proceedings of the ACM SAC, Symposium on Applied Computing (Madrid, Spain), 2002.
11. Qin Ding, Qiang Ding, and W. Perrizo, Association Rule Mining on Remotely Sensed Images Using P-trees. Proceedings of the PAKDD, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer-Verlag, Lecture Notes in Artificial Intelligence 2336, 66-79, May 2002.
12. S. Fujiwara, J. D. Ullman and R. Motwani, Dynamic Miss-Counting Algorithms: Finding Implications and Similarity Rules with Confidence Pruning. Proceedings of the IEEE ICDE (San Diego, CA), 2000.
13. F. Glover, "Tabu Search for Nonlinear and Parametric Optimization (with Links to Genetic Algorithms)." Discrete Applied Mathematics 49 (1-3): 231-255, 1994.
14. M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. Verkamo, Finding interesting rules from large sets of discovered association rules. Proceedings of the ACM CIKM, International Conference on Information and Knowledge Management (Kansas City, Missouri), 401-407, 1999.
15. C. Ordonez, C. Santana, and L. de Braal, Discovering Interesting Association Rules in Medical Data. Proceedings of the IEEE Advances in Digital Libraries Conference (Baltimore, MD), 1999.
16. W. Perrizo, Peano count tree technology lab notes. Technical Report NDSU-CS-TR-01-1, 2001. http://www.cs.ndsu.nodak.edu/-perrizo/classes/785/pct.html. January 2003.
17. Ron Raymon, An SE-tree based Characterization of the Induction Problem. Proceedings of the ICML, International Conference on Machine Learning (Washington, D.C.), 268-275, 1993.
18. N. Shivakumar, and H. Garcia-Molina, Building a Scalable and Accurate Copy Detection Mechanism. Proceedings of the International Conference on the Theory and Practice of Digital Libraries, 1996.
19. P. Tan, and V. Kumar, Interestingness Measures for Association Patterns: A Perspective, KDD'2000 Workshop on Post-processing in Machine Learning and Data Mining, Boston, 2000.
20. H.R. Varian, and P. Resnick, Eds. CACM Special Issue on Recommender Systems. Communications of the ACM 40 (1997).