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.
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Project supported by the National Natural Science Foundation of China (Nos. 10876036 and 70871111) and the Ningbo Natural Science Foundation, China (No. 2010A610113)
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Shen, B., Yao, M., Xie, Lj. et al. Mining item-item and between-set correlated association rules. J. Zhejiang Univ. - Sci. C 12, 96–109 (2011). https://doi.org/10.1631/jzus.C0910717
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DOI: https://doi.org/10.1631/jzus.C0910717
Key words
- Item-item and between-set correlated association rules
- All-confidence
- All-item-confidence
- Item-set correlation
- Mining algorithms
- Pruning effect