We study the following question: when can a mined pattern, which may be an association, a correlation, ratio rule, or any other, be regarded as interesting? Previous approaches to answering this question have been largely numeric. Speciically, we show that the presence of some rules may make others redundant, and therefore uninteresting. We articulate these principles and formalize them in the form of pruning rules. Pruning rules, when applied to a collection of mined patterns, can be used to eliminate redundant ones. As a concrete instance, we applied our pruning rules on association rules/positive association rules derived from a census database, and demonstrate that signiicant pruning results.