Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (12): 58-63.doi: 10.16180/j.cnki.issn1007-7820.2019.12.012

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

LI Cunjin1,SUN Hong1,2   

  1. 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)


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