基于MR的并行决策树分类算法的设计与实现

Design and Implementation of Parallel Decision Tree Classification Based on MapReduce

决策树分类方法是实现数据挖掘中分类任务的一种有效方法,但在大规模测试数据集上运行时其实现性能受到严重影响。本文设计和实现一种基于MapReduce架构的并行决策树分类算法。实验结果表明:基于MapReduce的决策树分类算法比同类算法在其他并行编程模型下的实现在计算节点较多的情况下能得到更优的性能。

Decision tree classification is an effective classification method in data mining,but its performance is severely affected by large dataset.This paper addresses the design and implementation of a parallel decision tree classification algorithm based on MapReduce programming model.Experiment results show that this implementation works better than implementation based on other parallel programming models while running on more nodes.