基于Hadoop的Aprior算法改进及其在动车组运维的应用

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。

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

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.