International Journal of Computer and Information Technology (ISSN: 2279 – 0764) Volume 02– Issue 06, November 2013 www.ijcit.com 1110 Data Mining Techniques in EDM for Predicting the Performance of Students Ajay Kumar Pal Research Scholar, Sai Nath University, Ranchi, Jharkhand, India Saurabh Pal Head, Dept. of MCA, VBS Purvanchal University, Jaunpur, UP, India Email: drsaurabhpal {at} yahoo.co.in Abstract— In recent, growth of higher education has increased rapidly. Many new institutions, colleges and universities are being established by both the private and government sectors for the growth of education and welfare of the students. Each institution aims at producing higher and exemplary education rates by employing various teaching and grooming methods. But still there are cases of unemployment that exists among the medium and low risk students. This paper describes the use of data mining techniques to improve the efficiency of academic performance in the educational institutions. Various data mining techniques such as decision tree, association rule, nearest neighbors, neural networks, genetic algorithms, exploratory factor analysis and stepwise regression can be applied to the higher education process, which in turn helps to improve student’s performance. This type of approach gives high confidence to students in their studies. This method helps to identify the students who need special advising or counseling by the teacher which gives high quality of education. Keywords-component; Data Mining; KDD; EDM; Association Rule I. INTRODUCTION Data mining is the powerful technology for analyzing important information from the data warehouse. It is data analysis methodology used to identify hidden patterns in a large data set. Data mining is one of the steps in KDD process. Knowledge discovery (KDD) aims at the discovery of useful information from large collections of data [2]. The main goal of data mining in the KDD process concerned with the algorithmic means by which patterns or structures are enumerated from the data under acceptable computational efficiency limitations. Data mining 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 [3]. 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. There are increasing research interests in education field using data mining. Application of Data mining techniques concerns to develop the methods that discover knowledge from data and used to uncover hidden or unknown information that is not apparent, but potentially useful [8]. The data can be personal or academic which can be used to understand students behavior to assist instructors, to improve teaching, to improve curriculums and many other benefits. There are a large number of research papers on educational data mining discussing various problems within the higher education sector and providing examples for successful solutions was done by using data mining. C Romero and S Vetura [17] made a comprehensive study on the development of this educational data mining since 1995 to 2005. Their paper surveys the application of data mining to traditional education systems, particular web-based course, well known learning content management systems and adaptive and intelligent web- based educational systems. Mostly the problems attracting the attention of researchers are focused mainly on retention of students, more effective targeted marketing, improving institutional efficiency, and alumni management. This study investigates and compares the educational domain of data mining from data that come from students personal, social, psychological and other environmental variables. The scope of this research paper, makes to extract the knowledge discover from the student database for improving the student performance. Here by, data mining techniques including a rule learner (OneR), a common decision tree algorithm C4.5 (J48), a neural network (MultiLayer Perceptron), and a Nearest Neighbour algorithm (IB1) are used. II. BACKGROUND AND RELATED WORK Due to high accuracy and prediction quality data mining technique is widely used in different areas. Education sector is also enriched with the help of this technique. A number of