基于MapReduce的质检大数据关联规则挖掘  

Association Rules Mining of Quality Inspect Big Data Based on Map Reduce

作　　者：林宗缪[1] 郭先超[1] 姚文勇[1] 

机构地区：[1]上海市质量监督检验技术研究院,上海201114

出　　处：《自动化技术与应用》2016年第8期43-46,共4页Techniques of Automation and Applications

基　　金：上海市信息化发展专项资金项目(编号201201023)

摘　　要：面对日益突出的产品质量问题,如何针对政府监督和企业委托的产品质量检测的海量数据进行处理,从大数据中挖掘出潜在有用信息为政府和监管部门提供预警和决策支持?文中基于Map Reduce分布式计算框架对关联规则挖掘的Apriori算法进行了改进,并以质量检验数据为处理对象进行关联规则挖掘。该设计在对质检大数据集进行频繁项集挖掘时,充分利用Map Reduce的分布式计算的优势,从海量数据中挖掘出隐含的关联规则,提高了海量质检数据分析的效率,为质量监管决策提供了支持。In the face of increasingly prominent product quality problems, how can efficiently deal with massive data of quality inspection is produced by government supervision and enterprise commission ,to help government and public to enhance quality prediction, quality warning and decision support. The paper presents an improved association rules mining of Apriori algorithm, it is based on MapReduce. The algorithm takes the data of quality inspection as handling objects, the design takes advantage of MapReduce distributed computation to improve the efficiency of massive data analysis of qulity inspection, to provide support to quality supervision of govemment.

关 键 词：质量检验 大数据 关联规则 MAPREDUCE 

分 类 号：TP311.1[自动化与计算机技术—计算机软件与理论]