A MapReduce Implementation of C4.5 Decision Tree Algorithm

A MapReduce Implementation of C4.5 Decision Tree Algorithm

Recent years have witness the development of cloud computing and the big data era, which brings up challenges to traditional decision tree algorithms. First, as the size of dataset becomes extremely big, the process of building a decision tree can be quite time consuming. Second, because the data cannot fit in memory any more, some computation must be moved to the external storage and therefore...

PDTSSE: A Scalable Parallel Decision Tree Algorithm Based on MapReduce

Parallel decision tree learning is an effective and efficient approach to scaling the decision tree to large data mining application. Aiming at large scale decision tree learning, we present a novel parallel decision tree learning algorithm in MapReduce framework, called PDTSSE (Parallel Decision Tree via Sampling Splitting points with Estimation). We first propose an estimation method for samp...

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

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 learn...

MapReduce Minimum Spanning Tree Based Data With Mapreduce Implementation

A Multi-relational Decision Tree Learning Algorithm - Implementation and Experiments

We describe an efficient implementation (MRDTL-2) of the Multirelational decision tree learning (MRDTL) algorithm [19] which in turn was based on a proposal by Knobbe et al. [15] We describe some simple techniques for speeding up the calculation of sufficient statistics for decision trees and related hypothesis classes from multi-relational data. Because missing values are fairly common in many...