基于频次有效长度的加权关联规则挖掘算法研究  

Mining Algorithm for Weighted Association Rules Based on Frequency Effective Length

作　　者：张勇 李树青[1] 程永上[1] Zhang Yong;Li Shuqing;Cheng Yongshang(School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210046, China)

机构地区：[1]南京财经大学信息工程学院

出　　处：《数据分析与知识发现》2019年第7期85-93,共9页Data Analysis and Knowledge Discovery

基　　金：国家社会科学基金项目“基于大数据分析的数字图书馆个性化服务模式创新研究”(项目编号:16BTQ030)的研究成果之一

摘　　要：【目的】通过对数据库中项在重要程度上存在的差异性进行分析,解决传统关联规则挖掘算法挖掘大量冗余无价值规则的问题。【方法】在具有时态约束的序列上,结合频次有效长度方法挖掘非加权关联规则,引入加权方法,利用滑动窗口技术在时序序列上挖掘稀有加权关联规则。【结果】根据频次有效长度的加权关联规则挖掘算法所挖掘出的加权时序关联规则能够较为准确地进行推荐,推荐预测的准确度由62%提升至69%。【局限】由于滑动窗口每次滑动一个单位长度,加之窗口中生成的规则数量较多,导致挖掘算法在进行规则挖掘时执行时间较长。【结论】本文方法所挖掘出的加权时序关联规则能使推荐精度得到提升,并为关联规则挖掘方法提供新的研究思路。[Objective] This paper analyzes the differences in the importance of database items, aiming to address the issues of traditional association mining algorithm with redundant and worthless rules.[Methods] On the sequence with temporal constraints, we explored the non-weighted association rules with the frequency effective length and the weighting methods. Then, we used sliding window technique to study the rare weighted association rules on the time series.[Results] The accuracy of the prediction made by the proposed method increased to 69% from 62%.[Limitations] The mining algorithm took long time to extract the needed rules due to the sliding windows and the large number of rules generated.[Conclusions] The association rules of weighted time series improve the accuracy of recommendation, which also provides new directions for research method on association rules.

关 键 词：数据挖掘 关联规则 频次长度 滑动窗口 

分 类 号：G354[文化科学—情报学]