Study of Positive and Negative Association Rules Based on Multi-confidence and Chi-Squared Test

Using a single confidence threshold will result in a dilemmatic situation when simultaneously studying positive and negative association rule (PNAR), i.e., the forms A$\Rightarrow$B, A$\Rightarrow$¬B, ¬A$\Rightarrow$B and ¬A$\Rightarrow$¬B. A method based on four confidence thresholds for the four forms of PNARs is proposed. The relationships among the four confidences, which show the necessity of using multiple confidence thresholds, are also discussed. In addition, the chi-squared test can avoid generating misleading rules that maybe occur when simultaneously studying the PNARs. The method of how to apply chi-squared test in mining association rules is discussed. An algorithm PNARMC based on the chi-squared test and the four confidence thresholds is proposed. The experimental results demonstrate that the algorithm can not only generate PNARs rightly, but can also control the total number of rules flexibly.