Mining frequent closed patterns by adaptive pruning

Ruan Jian Xue Bao/Journal of Software

Volumn 15, Issue 1, 2004, Pages 94-102

  Liu, Jun Qiang ^a   Sun, Xiao Ying ^a   Zhuang, Yue Ting ^b   Pan, Yun He ^b  

^a Hangzhou Institute of Commerce   (China)

^b ZHEJIANG UNIVERSITY   (China)

Author keywords

Association rule; Data mining; Frequent closed pattern; Knowledge discovery

Indexed keywords

ADAPTIVE SYSTEMS; ARTIFICIAL INTELLIGENCE; FORMAL LOGIC; KNOWLEDGE ACQUISITION; PERFORMANCE; TREES (MATHEMATICS);

ASSOCIATION RULE; EFFICIENT PRUNING METHODS; FREQUENT CLOSED PATTERN; KNOWLEDGE DISCOVERY;

DATA MINING;

EID: 2542609071     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (8)

1. Pasquier N, Bastide Y, Taouil R, Lakhal L. Discovering frequent closed itemsets for association rules. In: Beeri C, et al, eds. Proc. of the 7th Int'l. Conf. on Database Theory. Jerusalem: Springer-Verlag, 1999. 398-416.
2. Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Beeri C, et al, eds. Proc. of the 20th Int'l. Conf. on Very Large Databases. Santiago: Morgan Kaufmann Publishers, 1994. 487-499.
3. Pei J, Han J, Mao R. CLOSET: An efficient algorithm for mining frequent closed itemsets. In: Gunopulos D, et al, eds. Proc. of the 2000 ACM SIGMOD Int'l. Workshop on Data Mining and Knowledge Discovery. Dallas: ACM Press, 2000. 21-30.
4. Burdick D, Calimlim M, Gehrke J. MAFIA: A maximal frequent itemset algorithm for transactional databases. In: Georgakopoulos D, et al, eds. Proc. of the 17th Int'l. Conf. on Data Engineering. Heidelberg: IEEE Press, 2001. 443-452.
5. Zaki MJ, Hsiao CJ. CHARM: An efficient algorithm for closed itemset mining. In: Grossman R, et al, eds. Proc. of the 2nd SIAM Int'l. Conf. on Data Mining. Arlington: SIAM, 2002. 12-28.
6. Liu JQ, Pan YH, Wang K, Han J. Mining frequent item sets by opportunistic projection. In: Hand D, et al, eds. Proc. of the 8th ACM SIGKDD Int'l. Conf. on Knowledge Discovery and Data Mining. Alberta: ACM Press, 2002. 229-238.
7. Srikant R. Quest synthetic data generation code. San Jose: IBM Almaden Research Center, 1994. http://www.almaden.ibm.com/software/quest/Resources/index.shtml
8. Blake C, Merz C. UCI Repository of machine learning. Irvine University of California, Department of Information and Computer Science, 1998. http://www.ics.uci.edu/-mlearn/MLRepository.html