摘要
目前的物流信息关联规则分析模型与方法难以适应分布、异构、动态的大数据环境。云关联挖掘实现了云计算与关联规则挖掘的结合,其核心是实现关联规则挖掘算法的Map Reduce并行化。本文在构建基于云关联挖掘的物流信息智能分析模式的基础上,以Apriori算法为例,探索了并行的物流信息关联规则分析算法及其实现,研究设计了Map Reduce并行化的Map函数、Combine函数和Reduce函数。最后,分析了本方法的优势。
The current analyzing model and method of logistics information association rule is difficult to adapt to the dis-tributed and heterogeneous data environment. Cloud association rule mining whose core is the Map Reduce parallelization ofassociation rule mining algorithm realizes the combination of cloud computing and classification mining. In this paper, theintelligent analyzing model of logistics information based on cloud association rule mining is constructed. Apriori algorithmis used as an example to explore the parallel analyzing algorithm of logistics information association rule and its implemen-tation. And the Map function and Reduce function of Map Reduce parallelization is designed. Finally, the advantages of theintelligent analyzing method of logistics information based on cloud association rule mining are analyzed.
出处
《情报科学》
CSSCI
北大核心
2016年第10期56-60,共5页
Information Science
基金
国家自然科学基金项目(71373197)
关键词
云挖掘
云关联挖掘
物流信息
物流信息智能分析
cloud mining
cloud association rule mining
logistics information
the intelligent analyzing of logistics information
