A new class of constraints for constrained frequent pattern mining
Carson Kai-Sang Leung, Lijing Sun · 2012
Most of the frequent pattern mining algorithms search for all frequent patterns. However, there are many real-life situations in which users are interested in only some tiny portions of the mined frequent patterns. For mining of constrained frequent patterns, several classes of user constraints---such as anti-monotone constraints---have been proposed and their properties have been exploited. In this paper, we introduce a new class of constraints called mixed monotone constraints. We exploit its property for effective mining of frequent patterns satisfying user constraints that sum both positive and negative numerical values.