广西师范大学学报（自然科学版）2011，Vol.29Issue(1)：82-86,5.
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
摘要
Abstract
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.关键词
MapReduce/决策树分类/SPRINTKey words
MapReduce/ decision tree classification/ SPRINT分类
信息技术与安全科学引用本文复制引用
朱敏,万剑怡,王明文..基于MR的并行决策树分类算法的设计与实现[J].广西师范大学学报（自然科学版）,2011,29(1):82-86,5.基金项目
国家自然科学基金资助项目(60963014) （60963014）
江西省自然科学基金项目(2008GZS0052) （2008GZS0052）
