Mining Weighted Negative Association Rules Based on Correlation from Infrequent Items

The every item is set a weight because there is different importance between items. Negative association rules become a focus in the field of data mining. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. The negative association rules often consist in the infrequent items. The negative rules mining is associated with weight, an algorithm is proposed to resolve the above problem in this paper. The experiment proves that the number of the negative association rules from the infrequent items is larger than those from the frequent.