An efficient biobjective evolutionary algorithm for mining frequent and high utility itemsets

Mining frequent and high utility itemsets (FHUIs) from transactional databases is essential in data mining. From a multiobjective perspective, modelling the task of mining FHUIs in a unified framework requires support and utility to be considered simultaneously. In contrast to traditional algorithms for mining FHUIs, multiobjective evolutionary algorithms (MOEAs) can overcome the difficulty of setting the parameter and can generate multiple solutions in one pass, which brings advantages to mining FHUIs. However, MOEAs may be inefficient when the number of transactions and the number of items in the transaction database are large. To address this problem, we propose an efficient biobjective evolutionary algorithm for obtaining FHUIs (BOEA-FHUI) based on three novel strategies. In BOEA-FHUI, a pruning strategy is proposed to reduce the search space. Based on the pruning results, a repair strategy is proposed to make the generated inferior offspring jump out of the dominated region of the previous Pareto solutions. With the proposed pruning and repair strategies, the search space can be significantly reduced, which helps improve the search efficiency. To increase the number of items with higher support and higher utility values, an improved mutation strategy based on the sparse nature of the FHUI is proposed, which can accelerate the convergence speed of the algorithm. The experimental results on the real-world and synthetic datasets show that the proposed algorithm performs better than state-of-the-art MOEAs in finding FHUIs.