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Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (2): 96-109    DOI: 10.1631/jzus.C0910717
    
Mining item-item and between-set correlated association rules
Bin Shen1, Min Yao*,2, Li-jun Xie3, Rong Zhu2, Yun-ting Tang1
1 Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China 2 School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 3 Center for Engineering & Scientific Computation, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
Mining item-item and between-set correlated association rules
Bin Shen1, Min Yao*,2, Li-jun Xie3, Rong Zhu2, Yun-ting Tang1
1 Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China 2 School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 3 Center for Engineering & Scientific Computation, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
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摘要： 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.
关键词： Item-item and between-set correlated association rules;  All-confidence;  All-item-confidence;  Item-set correlation;  Mining algorithms;  Pruning effect    
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.
Key words: Item-item and between-set correlated association rules    All-confidence    All-item-confidence    Item-set correlation    Mining algorithms    Pruning effect
收稿日期: 2009-11-19 出版日期: 2011-02-08
CLC:  TP311  
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Bin Shen
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引用本文:

Bin Shen, Min Yao, Li-jun Xie, Rong Zhu, Yun-ting Tang. Mining item-item and between-set correlated association rules. Front. Inform. Technol. Electron. Eng., 2011, 12(2): 96-109.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C0910717    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I2/96

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