MR-Tree - A Scalable MapReduce Algorithm for Building Decision Trees

Abstract

Learning decision trees against very large amounts of data is not practical on single node computers due to the huge amount of calculations required by this process. Apache Hadoop is a large scale distributed computing platform that runs on commodity hardware clusters and can be used successfully for data mining task against very large datasets. This work presents a parallel decision tree learning algorithm expressed in MapReduce programming model that runs on Apache Hadoop platform and has a very good scalability with dataset size.

Keywords

• big data
• decision tree
• Hadoop
• MapReduce
• pattern recognition