Genetic algorithm versus memetic algorithm for association rules mining

This paper deals with association rules mining using evolutionary algorithms. All previous bio-inspired based association rules mining approaches generate non admissible rules, which cannot be exploited by the end-user. To cope with this issue, we propose two approaches that avoid non admissible rules by developing a new strategy called delete and decomposition strategy. If an item appears in the antecedent and the consequent parts of a given rule, the latter is decomposed in two admissible rules. Then, we delete such item from the antecedent part of the first rule and from the consequent part of the second rule. Afterwards, we design a genetic algorithm called IARMGA and a memetic algorithm called IARMMA for association rules mining. Several experiments were carried out using both synthetic and reals instances. The results reveal a compromise between the execution time and the quality of output rules. IARMGA is faster than IARMMA whereas the latter outperforms the former in terms of rules quality.