Mining frequent item sets by opportunistic projection

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2002, Pages 229-238

  Liu, Junqiang ^a,d   Pan, Yunhe ^a   Wang, Ke ^b   Han, Jiawei ^c  

^a ZHEJIANG UNIVERSITY   (China)

^b SIMON FRASER UNIVERSITY   (Canada)

^c UIUC   (United States)

^d ZHEJIANG GONGSHANG UNIVERSITY   (China)

Author keywords

Association rules; Frequent patterns

Indexed keywords

ALGORITHMS; DATA STORAGE EQUIPMENT; DATA STRUCTURES; DATABASE SYSTEMS; RESPONSE TIME (COMPUTER SYSTEMS); SET THEORY;

ASSOCIATION RULES; CENTRAL PROCESSING UNIT;

DATA MINING;

EID: 0242709391     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/775047.775081     Document Type: Conference Paper

References (18)

1. R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent itemsets. In Journal of Parallel and Distributed Computing (Special Issue on High Performance Data Mining), 2000.
2. R. Agarwal, C. Aggarwal, and V. V. V. Prasad. Depth first generation of long patterns, in Proceedings of SIGKDD Conference, 2000.
3. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In SIGMOD'93, Washington, D.C., May 1993.
4. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB'94, pp. 487-499, Santiago, Chile, Sept. 1994.
5. R.J. Bayardo. Efficiently mining long patterns from databases. In SIGMOD'98, pp. 85-93, Seattle, Washington, June 1998.
6. D. Burdick, M. Calimlim, J. Gehrke. MAFIA: A maximal frequent itemset algorithm for transactional databases. In proceedings of the 17th Internation Conference on Data Engineering, Heidelberg, Germany, April 2001.
7. Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur. Dynamic Itemset Counting and Implication Rules for Market Basket Analysis. In SIGMOD'97, 255-264. Tucson, AZ, May 1997.
8. J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In VLDB'95, Zuich, Switzerland, Sept. 1995.
9. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In SIGMOD'2000, Dallas, TX, May 2000.
10. 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, March 1998.
11. J.S.Park, M.S.Chen, and P.S.Yu. An effective hash based algorithm for mining association rules. In Proc. 1995 ACM-SIGMOD, 175-186, San Jose, CA, Feb. 1995.
12. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang, H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases, Proc. 2001 Int. Conf. on Data Mining (ICDM'01), San Jose, CA, Nov. 2001.
13. Ashok Sarasere, Edward Omiecinsky, and Shamkant Navathe. An efficient algorithm for mining association rules in large databases. In 21st Int'l Conf. on Very Large Databases (VLDB), Zurich, Switzerland, Sept. 1995.
14. H. Toivonen. Sampling large databases for association rules. In Proc. 1996 Int. Conf. Very Large Data Bases (VLDB'96), 134-145, Bombay, India, Sept. 1996.
15. Zijian Zheng, Ron Kohavi and Llew Mason. Real World Performance of Association Rule Algorithms. In Proc. 2001 Int. Conf. on Knowledge Discovery in Databases (KDD'01), San Francisco, California, Aug. 2001.
16. http://fuzzy.cs.uni-magdeburg.de/~borgelt/src/apriori.exe
17. http://www.almaden.ibm.com/cs/quest/syndata.html
18. http://www.ics.uci.edu/~mlearn/MLRepository.html