Please wait a minute...
ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2004, Vol. 5 Issue (1): 8-15    DOI: 10.1631/jzus.2004.0008
Computer & Information Science     
An efficient algorithm for mining closed itemsets
LIU Jun-qiang, PAN Yun-he
Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China; Hangzhou University of Commerce, Hangzhou 310035, China
Download:     PDF (0 KB)     
Export: BibTeX | EndNote (RIS)      

Abstract  This paper presents a new efficient algorithm for mining frequent closed itemsets. It enumerates the closed set of frequent itemsets by using a novel compound frequent itemset tree that facilitates fast growth and efficient pruning of search space. It also employs a hybrid approach that adapts search strategies, representations of projected transaction subsets, and projecting methods to the characteristics of the dataset. Efficient local pruning, global subsumption checking, and fast hashing methods are detailed in this paper. The principle that balances the overheads of search space growth and pruning is also discussed. Extensive experimental evaluations on real world and artificial datasets showed that our algorithm outperforms CHARM by a factor of five and is one to three orders of magnitude more efficient than CLOSET and MAFIA.

Key words： Knowledge discovery      Data mining      Frequent closed patterns      Association rules     
Received: 25 October 2002     
CLC:  TP311  
  TP391  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
LIU Jun-qiang
PAN Yun-he
Cite this article:

LIU Jun-qiang, PAN Yun-he. An efficient algorithm for mining closed itemsets. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2004, 5(1): 8-15.

URL:

http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2004.0008     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2004/V5/I1/8

[1] Feng LI, Jin MA, Jian-hua LI. Distributed anonymous data perturbation method for privacy-preserving data mining[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(7): 952-963.
[2] Tao JIANG, Yu-cai FENG, Bin ZHANG, Zhong-sheng CAO, Ge FU, Jie SHI. Monitoring correlative financial data streams by local pattern similarity[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(7): 937-951.
[3] Qian YE, Ling CHEN, Gen-cai CHEN. Personal continuous route pattern mining[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(2): 221-231.
[4] Yi-jun BEI, Gang CHEN, Jin-xiang DONG, Ke CHEN. Bottom-up mining of XML query patterns to improve XML querying[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2008, 9(6): 744-757.
[5] LI Guo-qi, SHENG Huan-ye. Classification analysis of microarray data based on ontological engineering[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2007, 8(4): 638-643.
[6] Ma Yu-Liang, Yan Wen-Jun. Value reduction algorithm in rough sets based on association rules support[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2006, 7(Supplement 2): 219-222.