基于频繁模式树的大数据关联规则自动挖掘算法  

Automatic mining algorithm of association rules for big data based on frequent pattern trees

作　　者：王景兰[1] 王振 WANG Jinglan;WANG Zhen(Department of Information Engineering,Bozhou Vocational and Technical College,Bozhou 236800,Anhui,China;Department of Intelligent Manufacturing,Bozhou Gongye Xuexiao,Bozhou 236800,Anhui,China)

机构地区：[1]亳州职业技术学院信息工程系,安徽亳州236800 [2]亳州工业学校智能制造部,安徽亳州236800

出　　处：《上海电机学院学报》2023年第6期356-360,共5页Journal of Shanghai Dianji University

基　　金：安徽省高等学校省级质量工程研究项目(2022jnds039,2022jyxm1042);安徽省高校人文科学研究项目(SK2020A0778,2022AH052579);安徽省高校自然科学研究项目(KJ2021A1430,KJ2021ZD0162);亳州职业技术学院质量工程研究项目(2021jyxm0981)。

摘　　要：由于传统大数据挖掘算法效率较低,导致挖掘出的规则存在大量冗余,提出基于频繁模式树的大数据关联规则自动挖掘算法。通过扫描数据库生成频繁模式树,作为算法的实现依据,并在频繁模式树上挖掘大数据频繁模式设计关联规则自动挖掘算法。结果表明:本文算法仅需5.24s就能有效挖掘出高校学生缴费数据中的关联规则,验证了该算法运行效率较高。Due to the low efficiency of traditional big data mining algorithms,which leads to a large amount of redundancy in the mined rules,an automatic mining algorithm of association rules for big data based on frequent pattern trees is proposed.The frequent pattern tree is generated by scanning the database as the basis for the algorithm implementation,and the automatic mining algorithm of the association rule is designed by mining the frequent patterns of big data on the frequent pattern tree.The results show that it takes only 5.24s for the proposed algorithm to effectively mine the association rules in the payment data of college students,which verifies that the algorithm has high operating efficiency.

关 键 词：数据挖掘 频繁模式树 关联规则 自动挖掘算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]