Data Mining Methodologies to Study Student's Academic Performance Using the C4.5 Algorithm

Abstract

The study placed a particular emphasis on the so called data mining algorithms, but focuses the bulk of attention on the C4.5 algorithm. Each educational institution, in general, aims to present a high quality of education. This depends upon predicting the students with poor results prior they entering in to final examination. Data mining techniques give many tasks that could be used to investigate the students' performance. The main objective of this paper is to build a classification model that can be used to improve the students' academic records in Faculty of Mathematical Science and Statistics. This model has been done using the C4.5 algorithm as it is a well-known, commonly used data mining technique. The importance of this study is that predicting student performance is useful in many different settings. Data from the previous students' academic records in the faculty have been used to illustrate the considered algorithm in order to build our classification model.

Processing GIS Data Using Decision Trees and an Inductive Learning Method

“…C4.5 improves ID3 which cannot process continuous numeric problem. J48 is an open source Java implementation of the C4.5 algorithm in the WEKA data mining tool [14].…”

“…Hence, to achieve this construction, it is necessary to calculate the entropy of each feature of the training images by using C4.5 algorithm and measure the information gained for each feature. The attribute with the highest normalized information gain is chosen to make the decision [14]. The C4.5 algorithm then recurses on the partitioned sublists.…”

The Design of Predictive Model for the Academic Performance of Students at University Based on Machine Learning

“…An optimal hyperplane was defined to maximize the margin that separates the two classes as shown in Eq. (5).…”