中文摘要：

提出了一种基于相关性的框架.在此框架内,使用残差分析来判断两项之间是否是独立的.残差分析可以使得我们很容易地获得包含负蕴含规则在内的真正的关联（而不是并发）规则,而且不需要指定支持度和可信度阈值.为了提高发现规则的质量,文中使用遗传算法来发现优化规则.在人工数据集和真实数据集上的实验结果表明文中的算法在发现规则的有趣性上优于类Apriori算法.

英文摘要：

This paper proposes a framework based on correlation. In this framework, residual analysis is used to determine whether two itemsets are independent of each other. The measure can help us find really correlated rules instead of concurrent ones. In addition, negatively correlated rules also can be found with residual analysis. Moreover, the support and confidence thresholds not have to be specified in the framework. To improve the quality of rules, the authors employ genetic algorithm to find optimized rules. By running the algorithm on synthetic and real datasets, the author argue that the algorithm outperform over Apriori-like algorithm on the interestingness of rules.