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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.2 P.96-109

http://doi.org/10.1631/jzus.C0910717


Mining item-item and between-set correlated association rules


Author(s):  Bin Shen, Min Yao, Li-jun Xie, Rong Zhu, Yun-ting Tang

Affiliation(s):  Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China, School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China, Center for Engineering & Scientific Computation, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   tsingbin@zju.edu.cn, myao@zju.edu.cn

Key Words:  Item-item and between-set correlated association rules, All-confidence, All-item-confidence, Item-set correlation, Mining algorithms, Pruning effect


Bin Shen, Min Yao, Li-jun Xie, Rong Zhu, Yun-ting Tang. Mining item-item and between-set correlated association rules[J]. Journal of Zhejiang University Science C, 2011, 12(2): 96-109.

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author="Bin Shen, Min Yao, Li-jun Xie, Rong Zhu, Yun-ting Tang",
journal="Journal of Zhejiang University Science C",
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pages="96-109",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C0910717"
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%A Min Yao
%A Li-jun Xie
%A Rong Zhu
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%DOI 10.1631/jzus.C0910717

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T1 - Mining item-item and between-set correlated association rules
A1 - Bin Shen
A1 - Min Yao
A1 - Li-jun Xie
A1 - Rong Zhu
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DOI - 10.1631/jzus.C0910717


Abstract: 
To overcome the failure in eliminating suspicious patterns or association rules existing in traditional association rules mining, we propose a novel method to mine item-item and between-set correlated association rules. First, we present three measurements: the association, correlation, and item-set correlation measurements. In the association measurement, the all-confidence measure is used to filter suspicious cross-support patterns, while the all-item-confidence measure is applied in the correlation measurement to eliminate spurious association rules that contain negatively correlated items. Then, we define the item-set correlation measurement and show its corresponding properties. By using this measurement, spurious association rules in which the antecedent and consequent item-sets are negatively correlated can be eliminated. Finally, we propose item-item and between-set correlated association rules and two mining algorithms, I&ISCoMine_AP and I&ISCoMine_CT. Experimental results with synthetic and real retail datasets show that the proposed method is effective and valid.

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

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