Research on Mining Positive and Negative Association Rules Based on Dual Confidence

摘要

Mining of association rules has become an important area in the research on data mining. However the traditional approaches based on support-confidence framework maybe generate a great number of redundant and wrong association rules. In order to solve the problems, a correlation measure is defined and added to the mining algorithm for association rules. According to the value of correlation measure, association rules are classified into positive and negative association rules. Therefore, the new algorithm can mine the negative-item-contained rules. In the paper, the algorithm which based on the correlation and dual confidence, can mine the positive and negative association rules. The experimental result shows that positive and negative association rules mining algorithm can reduce the scale of meaningless association rules, and mine a lot of interesting negative association rules.