Performance Evaluation of Entorpy and Gini Using Threaded and Non-threaded ID3 on Anaemia Dataset

Classification is an important data mining task, and decision trees have emerged as a popular classifier due to their simplicity and relatively low computational complexity. Time required to build a decision tree becomes intractable, as datasets get extremely large. To overcome this problem we proposed a parallel mode of ID3 algorithm. Decision tree building is well-suited for thread-level parallelism as it requires a large number of independent computations. In this paper, we present the analysis and parallel implementation of the ID3 algorithm using Entropy and Gini as heuristics, along with experimental results conducted on the anaemic patient's data set.