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

电子科技 ›› 2019, Vol. 32 ›› Issue (12): 58-63.doi: 10.16180/j.cnki.issn1007-7820.2019.12.012

改进自适应遗传算法在关联规则中的研究

李存进¹,孙红^1,²

1.上海理工大学光电信息与计算机工程学院,上海 200093
2.上海现代光学系统重点实验室,上海 200093

收稿日期:2018-11-26 出版日期:2019-12-15 发布日期:2019-12-24
作者简介:李存进(1993-),男,硕士研究生。研究方向:大数据与云计算、图像处理。|孙红(1964-),女,博士,副教授。研究方向:大数据与云计算、控制科学与工程、模式识别与智能系统。
基金资助:
国家自然科学基金(61472256);国家自然科学基金(61170277);国家自然科学基金(61703277);上海市教委科研创新重点项目(12zz137);沪江基金(C14002)

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

LI Cunjin¹,SUN Hong^1,²

1.School of Optical Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093, China
2.Shanghai Key Lab of Modern Optical System, Shanghai 200093,China

Received:2018-11-26 Online:2019-12-15 Published:2019-12-24
Supported by:
National Natural Science Foundation of China(61472256);National Natural Science Foundation of China(61170277);National Natural Science Foundation of China(61703277);Key Project of Scientific Research and Innovation of Shanghai Education Commission(12zz137);HJ Fund(C14002)

摘要/Abstract

摘要：

针对传统的关联规则在大数据环境下数据挖掘过程中效率低的问题,文中融入了遗传算法,并且对传统的遗传算法进行优化以便更好地适用于关联规则的挖掘。针对传统的自适应遗传算法,对其交叉算子和变异算子进行自适应优化,在此基础上加入兴趣度用来剔除一些没有实际效果的规则。实验结果表明在关联规则提取方面,结合云计算技术和所改进的算法可以优化其挖掘效率,并克服适应性和可用性等方面的缺陷,不仅加快了算法的收敛速度,也提高了解的质量。

关键词: 数据挖掘, 关联规则, 自适应遗传算法, 兴趣度, 云计算, 挖掘效率

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

中图分类号:

TP301.6

李存进,孙红. 改进自适应遗传算法在关联规则中的研究[J]. 电子科技, 2019, 32(12): 58-63.

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.

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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)
	…	…	…	…	…	…

改进自适应遗传算法在关联规则中的研究

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

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