Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 220700200-8.doi: 10.11896/jsjkx.220700200

• Big Data & Data Science • Previous Articles     Next Articles

Bio-inspired Frequent Itemset Mining Strategy Based on Genetic Algorithm

ZHAO Xuejian1,2, ZHAO Ke1   

  1. 1 Technology and Application Engineering Center of Postal Big Data,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
    2 Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
  • Published:2023-11-09
  • About author:ZHAO Xuejian,born in 1982,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include data mining and wireless sensor networks.
  • Supported by:
    National Natural Science Foundation of China(61972208) and China Postdoctoral Science Foundation(2018M640509).

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Abstract: Precise frequent itemset mining algorithms usually have a low time efficiency,particularly in processing large-scale data sets.To solve this problem,a frequent itemset mining algorithm,genetic algorithm combining apriori property based frequent itemset mining(GAA-FIM),is proposed,which combines the genetic algorithm and the downward closure property of precise frequent itemset mining algorithms.The detailed operation rules of coding operation,crossover operation,mutation operation and selection operation are described in detail.In GAA-FIM algorithm,individuals with good genes are preferentially added to the latest generation of candidate population through the asexual crossover operation process and the scale of the new generation candidate can be expanded through mutation operation.Therefore,the time efficiency of the proposed algorithm can be improved greatly and the frequent itemsets with better quality can be obtained.The performance of GAA-FIM algorithm is validated based on both synthetic data sets and real data sets.Experimental results show that the proposed GAA-FIM algorithm has a better time efficiency than GAFIM algorithm and GA-Apriori algorithm.Moreover,the quality of mining frequent itemsets has been further improved.

Key words: Frequent itemset, Genetic algorithm, Bio-inspired, Downward closure property, Data mining

CLC Number: 

  • TP391
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