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- W4379117003 abstract "Association rule learning is to mine the correlation between items and objects from certain data or other information carriers, and the results can reveal the hidden association patterns in the data. This paper firstly analyzes the advantages and disadvantages of the traditional particle swarm optimization algorithm, and finds the existing problems. Aiming at these problems, this paper improves the particle swarm optimization algorithm based on the random search theory of complex groups. In this paper, the improved fusion particle swarm optimization algorithm is used in the association rule mining model, and more effective rules can be obtained. Experiments show that the improved fusion particle swarm optimization algorithm proposed in this paper is feasible and efficient in mining association rules." @default.
- W4379117003 created "2023-06-03" @default.
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- W4379117003 date "2023-01-01" @default.
- W4379117003 modified "2023-10-10" @default.
- W4379117003 title "A Novel Association Rules Mining Based on Improved Fusion Particle Swarm Optimization Algorithm" @default.
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- W4379117003 doi "https://doi.org/10.1007/978-981-99-0880-6_12" @default.
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