Feasible Itemset Distributions in Data Mining: Theory and Application

Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems

Volumn 22, Issue , 2003, Pages 284-295

  Ramesh, Ganesh ^a   Maniatty, William A ^a   Zaki, Mohammed J ^b  

^a STATE UNIVERSITY OF NEW YORK   (United States)

^b Rensselaer Polytechnic Institute   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BENCHMARKING; DATA PRIVACY; DATABASE SYSTEMS; DNA; PROBLEM SOLVING; PROTEINS;

DATASETS; FEASIBLE ITEMSET;

DATA MINING;

EID: 1142299753     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. R. Agrawal, C. Aggarwal, and V. Prasad. Depth First Generation of Long Patterns. In 7th Int'l Conference on Knowledge Discovery and Data Mining, Aug. 2000.
2. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In U. Fayyad and et al, editors, Advances in Knowledge Discovery and Data Mining, pages 307-328. AAAI Press, Menlo Park, CA, 1996.
3. S. Bay. The UCI KDD Archive (kdd.ics.uci.edu). University of California, Irvine. Department of Information and Computer Science.
4. R. J. Bayardo. Efficiently mining long patterns from databases. In ACM SIGMOD Conf. Management of Data, June 1998.
5. B. Bollobás. Combinatorics. Cambridge University Press, 1986.
6. S. Brin, R. Motwani, J. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. In ACM SIGMOD Conf. Management of Data, May 1997.
7. D. Burdick, M. Calimlim, and J. Gehrke. MAFIA; a maximal frequent itemset algorithm for transactional databases. In Intl. Conf. on Data Engineering, Apr. 2001.
8. B. Goethals, F. Geerts, and J. V. den Bussche. A tight upper bound on the number of candidate patterns. In 1st IEEE International Conference on Data Mining, Nov. 2001.
9. K. Gouda and M. J. Zaki. Efficiently mining maximal frequent itemsets. In 1st IEEE Int'l Conf. on Data Mining, Nov. 2001.
10. D. Gunopulos, R. Khardon, H. Mannila, and H. Toivonen. Data mining, hypergraph transversals, and machine learning. In 16th ACM Symp. Principles of Database Systems, May 1997.
11. J. Han and M. Kamber. Data Mining: Concepts and Techniuges. Morgan Kaufmann Publishers, San Francisco, CA, 2001.
12. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In ACM SIGMOD Conf. Management of Data, May 2000.
13. G. Katona. A theorem of finite sets. In P. Erdos and G. Katona, editors, Theory of Graphs, pages 187-207. Akademiai Kiado, Budapest, 1968.
14. D.-I. Lin and Z. M. Kedem. Pincer-search: A new algorithm for discovering the maximum frequent set. In 6th Intl. Conf. Extending Database Technology, Mar. 1998.
15. A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In 21st VLDB Conf., 1995.
16. M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New algorithms for fast discovery of association rules. In 3rd Intl. Conf. on Knowledge Discovery and Data Mining, Aug. 1997.
17. Z. Zheng, R. Kohavi, and L. Mason. Real world performance of association rule algorithms. In 7th Intl. Conf. on Knowledge Discovery and Data Mining, Aug. 2001.