计算机工程与应用2019，Vol.55Issue(11)：257-264,8.DOI:10.3778/j.issn.1002-8331.1803-0236
基于并行Apriori的物流路径频繁模式研究
Research on Logistics Path Frequent Patterns Based on Parallel Apriori
摘要
Abstract
The traditional method of frequent path mining analysis is realized by the association rule algorithm. However, when dealing with large data sets, the traditional association rules algorithm will take up too much memory and process data slowly. In this paper, a parallel Apriori algorithm based on Fuzzy c-means clustering algorithm is proposed. The model performs clustering analysis of the original data set by Fuzzy c-means algorithm, divides the logistics path data which is considered as the same district into a data cluster with high similarity. Then the model utilizes the Apriori algorithm to mine the frequent paths in this district, so as to obtain the frequent logistics path of each area. Meanwhile, the algorithm is parallelized through the Hadoop platform, which can effectively improve the efficiency and the quality of the algorithm. Through the analysis of the frequent path of logistics, managers can better understand the flow of goods and make the de-cision of the optimization of the delivery path.关键词
大数据/频繁路径/Hadoop/Fuzzy c-means聚类算法/Apriori算法Key words
big data/frequent path/Hadoop/Fuzzy c-means clustering algorithm/Apriori algorithm分类
信息技术与安全科学引用本文复制引用
曹菁菁,任欣欣,徐贤浩..基于并行Apriori的物流路径频繁模式研究[J].计算机工程与应用,2019,55(11):257-264,8.基金项目
国家自然科学基金重点国际(地区)合作与交流项目(No.71620107002) （地区）
国家自然科学基金青年项目(No.61502360). （No.61502360）
