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Received: 2005-06-11

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.2 P.216~224

http://doi.org/10.1631/jzus.2006.A0216


A novel algorithm for frequent itemset mining in data warehouses


Author(s):  Xu Li-jun, Xie Kang-lin

Affiliation(s):  Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030, China

Corresponding email(s):   lijunxu@sjtu.edu.cn

Key Words:  Frequent itemset, Close itemset, Star schema, Dimension table, Fact table


Xu Li-jun, Xie Kang-lin. A novel algorithm for frequent itemset mining in data warehouses[J]. Journal of Zhejiang University Science A, 2006, 7(2): 216~224.

@article{title="A novel algorithm for frequent itemset mining in data warehouses",
author="Xu Li-jun, Xie Kang-lin",
journal="Journal of Zhejiang University Science A",
volume="7",
number="2",
pages="216~224",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0216"
}

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%T A novel algorithm for frequent itemset mining in data warehouses
%A Xu Li-jun
%A Xie Kang-lin
%J Journal of Zhejiang University SCIENCE A
%V 7
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%P 216~224
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0216

TY - JOUR
T1 - A novel algorithm for frequent itemset mining in data warehouses
A1 - Xu Li-jun
A1 - Xie Kang-lin
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 2
SP - 216
EP - 224
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A0216


Abstract: 
Current technology for frequent itemset mining mostly applies to the data stored in a single transaction database. This paper presents a novel algorithm MultiClose for frequent itemset mining in data warehouses. MultiClose respectively computes the results in single dimension tables and merges the results with a very efficient approach. close itemsets technique is used to improve the performance of the algorithm. The authors propose an efficient implementation for star schemas in which their algorithm outperforms state-of-the-art single-table algorithms.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[6] Inmon, W.H., 1996. Building the Data Warehouse, 2nd Edition. Wiley, Chichester.

[7] Jensen, V.C., Soparkar, N., 2000. Frequent Itemset Counting across Multiple Tables. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, p.49-61.

[8] Ng, K., Fu, W., Wang, K., 2002. Mining Association Rules from Stars. Proceedings of the IEEE International Conference on Data Mining, p.322-329.

[9] Pasquier, N., Bastide, Y., Taouil, R., Lakha, L., 1999. DisCovering Frequent Closed Itemsets for Association Rules. Proceedings of the 7th International Conference on Database Theory, p.398-416.

[10] Pei, J., Han, J., Mao, R., 2000. CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets. Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, p.21-30

[11] Shenoy, P., Haritsa, J.R., Sundarshan, S., Bhalotia, G., Bawa, M., Shah, D., 2000. Turbo-charging Vertical Mining of Large Databases. Proceedings of ACM SIGMOD International Conference on Management of Data, p.22-33.

[12] Zaki, M.J., Hsiao, C.J., 2002. CHARM: An Efficient Algorithm for Closed Itemset Mining. Proceedings of SIAMOD International Conference on Data Mining, p.457-473.

[13] Zaki, M.J., Gouda, K., 2003. Fast Vertical Mining Using Diffsets. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p.326-335.

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