前后部项约束关联规则并行化算法  

Parallel algorithm for fore-part and rear-part item-constrained association rules

作　　者：孟月昊 冯文 林荣霞 陈铭师 Meng Yuehao;Feng Wen;Lin Rongxia;Chen Mingshi(32753Army,Wuhan,Hubei 430010,China)

机构地区：[1]32753部队,湖北武汉430010

出　　处：《计算机时代》2021年第8期1-7,共7页Computer Era

摘　　要：为了解决大规模数据环境下挖掘出的关联规则过多,用户需要耗费大量时间在这些关联规则中寻找自己感兴趣规则的问题,提出了一种基于Map/Reduce并行化编程模型的前后部项约束关联规则挖掘算法FRPFP。通过对用户感兴趣的规则前后部项进行标记和分组挖掘,并在各分组挖掘过程中根据标记的规则前后部约束项,对事务集进行压缩,从而筛选出有效的频繁项集,最终得到含有用户感兴趣项的关联规则。该算法在Spark框架中实现,实验结果表明,该算法能够有效地减少冗余规则的产生,计算开销较少,具有较好的规模增长性。To solve the problem that too many association rules are mined in large-scale data environments,users need to spend a lot of time to find the rules they interested in,a fore-part and rear-part item-constrained association rule mining algorithm FRPFP based on Map/Reduce parallel programming model is proposed.By marking and grouping the fore-part and rear-part items of the rules of interest to the user,and compressing the transaction set according to the fore-part and rear-part constraint items of the tagged rules during the group mining process,a valid set of frequent items is filtered out,and the association rules containing the items of interest to the user are finally obtained.The algorithm is implemented in Spark framework,and the experimental results show that the algorithm can effectively reduce the generation of redundant rules,which has less computational overhead and has better scale growth.

关 键 词：项约束 关联规则 数据挖掘 FRPFP算法 

分 类 号：TP311.11[自动化与计算机技术—计算机软件与理论]