关键词: 大数据, 频繁路径, Hadoop, Fuzzy [c]-means聚类算法, Apriori算法

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 decision of the optimization of the delivery path.

Key words: big data, frequent path, Hadoop, Fuzzy [c]-means clustering algorithm, Apriori algorithm