Using flow graph network to mine non-redundant correlative rules

A correlative rule expresses a relationship between two correlative events happening one after another. These rules are potentially useful for analyzing correlative data, ranging from purchase histories, web logs and program execution traces. In this work, we investigate and propose a syntactic characterization of a non-redundant set of correlative rules built upon past work on compact set of representative patterns. When using the set of mined rules as a composite filter, replacing a full set of rules with a non-redundant subset of the rules does not impact the accuracy of the filter. Lastly, we propose an algorithm to mine this compressed set of non-redundant rules. A performance study shows that the proposed algorithm significantly improves both the runtime and compactness of mined rules over mining a full set of sequential rules.