An Efficient Recommender System for Predicting Study Track to Students Using Data Mining Techniques

Abstract: University or College admission is an intricate decision process but it is an important responsibility of the students to select the correct study track to succeed in their academic life. The complex issue is to assign students to the right academic field when they arrive at the end of basic education stage. In our research we propose a recommender system to students for their higher education which utilizes classification and clustering for recommending the right academic stream and colleges to the students. Instead of the traditional process the proposed system have many advantage such as high prediction accuracy, processing speed and flexibility etc. The decision tree classifier and fuzzy c-means clustering serves as the core design for the college and course prediction system. The system classifies the student and matches them to the proper study tracks according to their profile.

Keywords: Data Mining, Decision Tree, C4.5, Fuzzy C-Means, Recommender System.