An Effective Algorithm for Mining Positive and Negative Association Rules
Honglei Zhu, Zhigang Xu · 2008
Recently, mining negative association rules has received some attention and been proved to be useful in real world. This paper presents an efficient algorithm (PNAR) for mining both positive and negative association rules in databases. The algorithm extends traditional association rules to include negative association rules. When mining negative association rules, we adopt another minimum support threshold to mine frequent negative itemsets. With a correlation coefficient measure and pruning strategies, the algorithm can find all valid association rules quickly and overcome some limitations of the previous mining methods. The experimental results demonstrate its effectiveness and efficiency.