基于MapReduce的ID3决策树分类算法研究

决策树算法是经典的分类挖掘算法之一,具有广泛的实际应用价值。经典的ID3决策树算法是内存驻留算法,只能处理小数据集,在面对海量数据集时显得无能为力。为此,对经典ID3决策树生成算法的可并行性进行了深入分析和研究,利用云计算的MapReduce编程技术,提出并实现面向海量数据的ID3决策树并行分类算法。实验结果表明该算法是有效可行的。

Research on ID3 Decision Tree Classification Algorithm Based on MapReduce

Decision tree is widely used in data mining which is one of the typical classification algorithms.Traditional ID3 tree learning algorithms require training data to reside in memory on a single machine,so they cannot deal with massive datasets.To solve this problem,this paper analyzes the parallel algorithm of ID3 decision tree based on MapReduce model,then proposes a parallel and distributed algorithm for ID3 decision tree learning.The experimental results demonstrate the algorithm can scale well and efficiently process large-scale datasets on commodity computers.