A new framework for metaheuristic-based frequent itemset mining

作者：Djenouri, Youcef^*; Djenouri, Djamel; Belhadi, Asma; Fournier-Viger, Philippe; Lin, Jerry Chun-Wei

来源：Applied Intelligence, 2018, 48(12): 4775-4791.

DOI：10.1007/s10489-018-1245-8

• Frequent itemset mining
• Apriori
• Intelligent methods

This paper proposes a novel framework for metaheuristic-based Frequent Itemset Mining (FIM), which considers intrinsic features of the FIM problem. The framework, called META-GD, can be used to steer any metaheuristics-based FIM approach. Without loss of generality, three metaheuristics are considered in this paper, namely the genetic algorithm (GA), particle swarm optimization (PSO), and bee swarm optimization (BSO). This allows to derive three approaches, named GA-GD, PSO-GD, and BSO-GD, respectively. An extensive experimental evaluation on medium and large database instances reveal that PSO-GD outperforms state-of-the-art metaheuristic-based approaches in terms of runtime and solution quality.

• 出版日期2018-12
• 单位哈尔滨工业大学