A new framework for metaheuristic-based frequent itemset mining |
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Authors: | Youcef Djenouri Djamel Djenouri Asma Belhadi Philippe Fournier-Viger Jerry Chun-Wei Lin |
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Affiliation: | 1.IMADA, Computer Science Department,Southern Denmark University,Odense,Denmark;2.DTISI,CERIST Research Center,Algiers,Algeria;3.RIMA, USTHB,Algiers,Algeria;4.School of Humanities and Social Sciences,Harbin Institute of Technology (Shenzhen),Shenzhen,China;5.Department of Computing, Mathematics, and Physics,Western Norway University of Applied Sciences (HVL),Bergen,Norway |
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Abstract: | 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. |
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