改进自适应遗传算法在关联规则中的研究

doi:10.16180/j.cnki.issn1007-7820.2019.12.012

Abstract

Abstract:

Aiming at the low efficiency of traditional association rules in the data mining process under the big data environment, genetic algorithm was integrated. Meanwhile, the traditional genetic algorithm was also optimized to apply to the mining of association rules. Aiming at the traditional adaptive genetic algorithm, the crossover operator and mutation operator were optimized adaptively, and then the degree of interest was added to eliminate some rules that had no practical effect. Experimental results showed that combining cloud computing technology and the improved algorithm in the extraction of association rules could optimize its mining efficiency and overcame the shortcomings of adaptability and usability, which not only accelerated the convergence speed of the algorithm, but also improved the quality of understanding.

Key words: data mining, associationrule, self-adaptive genetic algorithm, interest, cloudcomputing, mining efficiency

CLC Number:

TP301.6

LI Cunjin,SUN Hong. The Study of Improved Self-adaptive Genetic Algorithm in Association Rules[J].Electronic Science and Technology, 2019, 32(12): 58-63.

Figures/Tables 10

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References 16

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要素类型	2020时降水量	平均风速	平均气温	…	平均水汽压	平均相对湿度
编码值	15(1)	10(1)	-132(1)	…	25(1)	27(1)
	33(2)	29(2)	93(2)	…	55(2)	44(2)
	58(3)	44(3)	10(3)	…	108(3)	79(3)
	…	…	…	…	…	…

The Study of Improved Self-adaptive Genetic Algorithm in Association Rules

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