Association rule mining often generates a huge number of rules, but a majority of them either are redundant or do not reflect the true correlation relationship among data objects. We re-examine this problem and show that two interesting measures, all-confidence (denoted as /spl alpha/) and coherence (denoted as /spl gamma/), both disclose genuine correlation relationships and can be computed efficiently. Moreover, we propose two interesting algorithms, CoMine(/spl alpha/) and CoMine(/spl 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.