基于遗传算法的生物启发频繁项集挖掘策略  

Bio-inspired Frequent Itemset Mining Strategy Based on Genetic Algorithm

作　　者：赵学健[1,2] 赵可 ZHAO Xuejian;ZHAO Ke(Technology and Application Engineering Center of Postal Big Data,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)

机构地区：[1]江苏省邮政大数据技术与应用工程中心(南京邮电大学),南京210003 [2]宽带无线通信与传感网技术教育部重点实验室(南京邮电大学),南京210003

出　　处：《计算机科学》2023年第S02期624-631,共8页Computer Science

基　　金：国家自然科学基金(61972208);中国博士后科学基金(2018M640509)。

摘　　要：精确频繁项集挖掘算法时间效率低下,在处理大规模数据集时力不从心。针对该问题,提出一种基于遗传算法的频繁项集挖掘策略GAA-FIM(Genetic Algorithm combining Apriori property based Frequent Itemset Mining),给出了编码操作、交叉操作、变异操作和选择操作的详细操作规则。该算法将遗传算法与精确频繁项集挖掘算法的向下闭包特性融合,改进了传统的有性繁殖的交叉操作方式,将具有良好遗传基因的个体优先加入到新一代候选种群中,并通过变异操作扩展新一代候选种群的规模,以提升算法的时间效率,获取更佳质量的频繁项集。基于合成数据集和真实数据集对GAA-FIM算法的性能进行了验证,实验结果表明GAA-FIM算法与GAFIM和GA-Apriori等算法相比具有更好的时间效率,频繁项集质量也得到了进一步提升。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.

关 键 词：频繁项集 遗传算法 生物启发 向下闭包特性 数据挖掘 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]