An association rule generation algorithm usually generates too many rules including a lot of uninteresting ones. Many interestingness criteria are proposed to prune those uninteresting rules. However, they work in post-pruning process and hence do not improve the rule generation ef?ciency. In this paper, we discuss properties of informative rule set and conclude that the informative rule set includes all interesting rules measured by many commonly used interestingness criteria, and that rules excluded by the informative rule set are forwardly prunable, i.e. they can be removed in the rule generation process instead of post pruning. Based on these properties, we propose a Direct Interesting rule Generation algorithm, DIG, to directly generate interesting rules de?ned by any of 12 interestingness criteria discussed in this paper. We further show experimentally that DIG is faster and uses less memory than Apriori.