Extracting Relationships between Students' Academic Performance and Their Area of Interest Using Data Mining Techniques

Data mining is a field of computer science that combines tools from artificial intelligence and statistics with database management. Data mining can be used in various fields of real life and one of the areas where it is applied and presented in this paper is on education. The findings provided by the use of data mining in education can help in the increase of education quality. In this study we applied data mining techniques to find classification rules between student academic performance and master program that they wish to attend as well as to partition students into clusters according to their characteristics such as academic performance. The extraction of classification rules and clustering are carried out using C4.5 decision tree and k-means algorithms respectively. The results of both techniques suggest helping students to focus on the area they are interested in.