Mining item-item and between-set correlated association rules

作者：Shen, Bin; Yao, Min^*; Xie, Li-jun; Zhu, Rong; Tang, Yun-ting

来源：Journal of Zhejiang University-Science C(Computers and Electronics), 2011, 12(2): 96-109.

DOI：10.1631/jzus.C0910717

• Item-item and between-set correlated association rules
• All-confidence
• All-item-confidence
• Item-set correlation
• Mining algorithms
• Pruning effect

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.

• 出版日期2011-2
• 单位浙江大学宁波理工学院; 浙江大学