Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 2, Issue. 2, February 2013, pg.53 – 57 RESEARCH ARTICLE © 2013, IJCSMC All Rights Reserved 53 An Empirical Study of Applications of Data Mining Techniques for Predicting Student Performance in Higher Education 1 Mahendra Tiwari, Research Scholar, (UPRTOU, Allahabad) 2 Randhir Singh, Assistant Professor, (UIM, Allahabad) 3 Neeraj Vimal, Assistant Professor, (Azad Inst.of Eng. & Technology, Lucknow) Abstract— Educational institutions are important parts of our society and playing a vital role for growth and development of nation and prediction of student’s performance in educational environments is also important as well. Student’s academic performance is based upon various factors like personal, social, psychological etc. Educational data mining concerns with developing methods for discovering knowledge from data, that comes from educational domain. The Data Mining tool has accepted as a decision making tool which is able to facilitate better resource utilization in terms of students performance. In this paper a student data from an engineering college has been taken and various data mining methods have been performed. This paper addresses the applications of data mining in educational institution to extract useful information from available data set and providing analytical tool to view. The result of study is aimed to develop a faith on data mining techniques so that present education system may adopt this as a strategic management tool. Indexed Terms: - Academic performance, Data mining, Data classification, Clustering, Student’s result database. I. INTRODUCTION Data mining is data analysis methodology used to identify hidden patterns in a large data set. It has been successfully used in different areas including the educational environment. Educational data mining is an interesting research area which extracts useful, previously unknown patterns from educational database for better understanding, improved educational performance and assessment of the student learning process [7]. Evaluating students’ performance is a complex issue, which can’t be restricted for the grading. Reasons of good or bad performances belong to the main interests of teachers, because they can plan and customize their teaching program, based on the feedback. Data mining is one of the approaches, which can provide an effective assistance in revealing complex relationships behind the grades [5]. Data miming consists of a set of techniques that can be used to extract relevant and interesting knowledge from data. Data mining has several tasks such as association rule mining, classification and prediction, and clustering. Classification techniques are supervised learning techniques that classify data item into predefined class label. It is one of the most useful techniques in data mining to build classification models from an input data set. The used classification techniques commonly build models that are used to predict future data trends.