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.