Association rule mining often generates a huge number of rules, but a majority of them either are redundant or don not reflect the tue correlation relationship among data objects. In this paper, we re-examine this problem and show that two interesting measures, all_confidence (denoted as \alpha) and coherence (denoted as \gamma), both disclose genuine correlation relationships and can be computed efficiently. Moreover, we propose two interesting algorithms, CoMine(\alpha) and CoMine(\gamma), based on extensions of a pattern-growth methodology. Our performance study shows that the CoMine algorithms have high performance in comparison with their Apriori-based counterpart algorithms.