In association rule mining, the process of extracting relations from a dataset often requires the application of more than one quality measure and, in many cases, such measures involve conflicting objectives. In such a situation, it is more appropriate to attain the optimal trade-off between measures. This paper deals with the association rule mining problem under a multi-objective perspective by proposing grammar guided genetic programming (G3P) models, that enable the extraction of both numerical and nominal association rules in only one single step. The strength of G3P is its ability to restrict the search space and build rules conforming to a given context-free grammar. Thus, the proposals presented in this paper combine the advantages of G3P models with those of multi-objective approaches. Both approaches follow the philosophy of two well-known multi-objective algorithms: the Non-dominated Sort Genetic Algorithm (NSGA-2) and the Strength Pareto Evolutionary Algorithm (SPEA-2).
In the experimental stage, we compare both multi-objective algorithms to a single-objective G3P proposal for mining association rules and perform an analysis of the mined rules. The results obtained show that multi-objective proposals obtain very frequent (with support values above 95% in most cases) and reliable (with confidence values close to 100%) rules when attaining the optimal trade-off between support and confidence. Furthermore, for the trade-off between support and lift, the multi-objective proposals also produce very interesting and representative rules.