Comparative Analysis of Decision Tree Classification Algorithms

Abstract

At the present time, the amount of data stored in educational database is increasing swiftly. These databases contain hidden information for improvement of student’s performance. Classification of data objects is a data mining and knowledge management technique used in grouping similar data objects together. There are many classification algorithms available in literature but decision tree is the most commonly used because of its ease of execution and easier to understand compared to other classification algorithms. The ID3, C4.5 and CART decision tree algorithms former applied on the data of students to predict their performance. But all these are used only for small data set and required that all or a portion of the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. This problem is solved by SPRINT and SLIQ decision tree algorithm. In serial implementation of SPRINT and SLIQ, the training data set is recursively partitioned using breadth-first technique. In this paper, all the algorithms are explained one by one. Performance and results are compared of all algorithms and evaluation is done by already existing datasets. All the algorithms have a satisfactory performance but accuracy is more witnessed in case of SPRINT algorithm.