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基于Hadoop的Aprior算法改进及其在动车组运维的应用

任广强, 舒敏

任广强, 舒敏. 基于Hadoop的Aprior算法改进及其在动车组运维的应用[J]. 铁路计算机应用, 2018, 27(10): 1-6.
引用本文: 任广强, 舒敏. 基于Hadoop的Aprior算法改进及其在动车组运维的应用[J]. 铁路计算机应用, 2018, 27(10): 1-6.
REN Guangqiang, SHU Min. Improvement of Aprior algorithm based on Hadoop and its application in operation and maintenance of EMU[J]. Railway Computer Application, 2018, 27(10): 1-6.
Citation: REN Guangqiang, SHU Min. Improvement of Aprior algorithm based on Hadoop and its application in operation and maintenance of EMU[J]. Railway Computer Application, 2018, 27(10): 1-6.

基于Hadoop的Aprior算法改进及其在动车组运维的应用

基金项目: 国家“八六三”高技术研究发展计划基金项目(2015AA043701)
详细信息
    作者简介:

    任广强,高级工程师;舒敏,在读硕士研究生

  • 中图分类号: U269+U279:TP39

Improvement of Aprior algorithm based on Hadoop and its application in operation and maintenance of EMU

  • 摘要: 论文着眼于解决大数据下的动车组关联规则挖掘问题,提出了一种基于Apriori算法改进的大数据关联规则挖掘算法T-MR-Apriori算法。该算法融合Hadoop技术,执行两遍MR分布式计算过程,完成整个关联规则挖掘流程,提高了海量数据下关联规则挖掘的效率和准确率。同时利用实际动车组运维数据进行验证,证明该算法在海量数据下具有良好的挖掘速度又能不降低挖掘性能。并且将该方法应用于动车组牵引电机运维数据挖掘,进行可视化展示。
    Abstract: This paper focused on solving the problem of EMU association rule mining under big data, proposed an improved T-MR-Apriori algorithm for association rule mining of big data based on Apriori algorithm. The improved algorithm combined Hadoop technology, performed two times MR distributed computing process, completed the whole process of association rule mining, improved the efficiency and accuracy of association rule mining under massive data. The actual EMU operation and maintenance data were used to verify the algorithm, which proved that the algorithm had good speed of mining in mass data and cannot reduce the performance of mining. The method would be applied to data mining and visualization of traction motor operation and maintenance in EMU.
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出版历程
  • 收稿日期:  2017-12-06
  • 刊出日期:  2018-10-24

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