Metaheuristics Guided by the Apriori Principle for Association Rule Mining

Association rule mining (ARM), one of the most known tasks in data mining, is considered as an optimization problem. The ARM problem can be solved either by exact methods or by metaheuristics. Exact methods such as Apriori algorithm are very efficient to deal with small and medium datasets. However, when dealing with large size datasets, these methods suffer from time complexity. Metaheuristics are proven faster but most of them suffer from accuracy. To deal with these two challenging issues, this work investigates to enhance metaheuristics and proposes hybrid approaches, which combine metaheuristics and the Apriori principle to intelligently explore the association rules space. To validate the proposed approaches the chemical reaction optimization metaheuristic (CRO) was used. Intensive experiments have been carried out and the first results are very promising in terms of accuracy and processing time.