Chemical Reaction Optimization Metaheuristic for Solving Association Rule Mining Problem
Abir Derouiche, Abdesslem Layeb, Zineb Habbas · 2017
Association Rule Mining (ARM) is one of the most important researched techniques of data mining. The aim of ARM is to extract interesting correlations, frequent patterns, associations or causal structures among set of items in transactional databases. Many exhaustive search algorithms have been proposed for solving ARM problem. However ARM problem is algorithmically complex to deal with, especially for large data sets. For this reason, metaheuristics are increasingly considered as a more promising alternative approach for solving ARM problem. This paper follows this direction and proposes a new approach for solving ARM by using the Chemical Reaction Optimization metaheuristic(CRO). The proposed approach has been tested on two transactional datasets: the booksdataset and the food items dataset. The experimental results were compared to two state-of-the-art algorithms, namely Apriori algorithm and FP-growth algorithm. It was also compared to the binary particle swarm optimization (BPSO) based association rule miner. In terms of quality, the obtained results show that our algorithm is efficient while it outperforms the other approaches in many cases.