基于多维关联规则的大规模数据并行挖掘研究  

Research on parallel mining of large⁃scale dat

作　　者：赵林燕 雷沁怡 洪德华 孙琦 刘翠玲 ZHAO Linyan;LEI Qinyi;HONG Dehua;SUN Qi;LIU Cuiling(Data Operations Center,State Grid Anhui Xintong Company,Hefei 230000,China)

机构地区：[1]国网安徽信通公司数据运营中心,安徽合肥230000

出　　处：《电子设计工程》2023年第24期159-162,167,共5页Electronic Design Engineering

摘　　要：为了解决因数据离散程度过大导致大规模数据并行挖掘质量变差的问题,提出基于多维关联规则的大规模数据并行挖掘方法。遵循多维关联思想建立关联树结构,根据RFM值计算公式完善多维运算法则,利用多维关联规则构建数据集合。求取近邻值指标、逆近邻值指标的数值,以此确定离散挖掘系数,结合该系数并行挖掘大规模数据。实验结果表明,在多维关联规则作用下,数据离散度取值小于35%,数据分布不再呈现稀疏状态,能有效提升大规模数据并行挖掘质量。In order to solve the problem of poor quality of large⁃scale data parallel mining caused by excessive data dispersion,a large⁃scale data parallel mining method based on multi⁃dimensional assoc⁃iation rules is proposed.Follow the multi⁃dimensional association idea to establish the association tree structure,improve the multi⁃dimensional algorithm according to the RFM value calculation formula,and use the multi⁃dimensional association rules to build the data set.Calculate the values of nearest neighbor value index and inverse nearest neighbor value index,so as to determine the discrete mining coefficient,and mine large⁃scale data in parallel with this coefficient.The experimental results show that under the action of multi⁃dimensional association rules,the value of data dispersion is less than 35%,and the data distribution is no longer sparse,which can effectively improve the quality of large⁃scale data parallel mining.

关 键 词：多维关联规则 大规模数据 并行挖掘 RFM值 近邻值 逆近邻值 

分 类 号：TN-9[电子电信]