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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.