GARM: Generalized Association Rule Mining

A thorough scrutiny of the literature dedicated to association rule mining highlights that a determined effort focused so far on mining the co-occurrence relations between items, i.e., conjunctive patterns. In this respect, disjunctive patterns presenting knowledge about complementary occurring items were neglected in the literature. Nevertheless, recently a growing number of works is shedding light on their importance for the sake of providing a richer knowledge for users. For this purpose, we propose in this paper a new tool, called GARM, aiming at building a partially ordered structure amongst some particular disjunctive patterns, namely the disjunctive closed ones. Starting from this structure, deriving generalized association rules, i.e., those offering conjunctive, disjunctive and negative connectors between items, becomes straightforward. Our experimental study put the focus on the mining performances as well as the quantitative aspect and proved the utility of the proposed approach.