International Journal of Computer Applications (0975 – 8887) Volume 39– No.7, February 2012 8 Combination of Clustering, Classification and Association Rule based Approach for Course Recommender System in E-learning Sunita B. Aher M.E. (CSE) -II Walchand Institute of Technology, Solapur University India Lobo L.M.R.J. Associate Professor, Head, Department of IT Walchand Institute of Technology, Solapur University India ABSTRACT Data mining also known as Knowledge Discovery in Database is the process of discovering new pattern from large data set. E-learning is the electronically learning & teaching process. Course Recommender System allows us to study the behavior of student regarding the courses. In Course Recommender System in E-learning, we collect the data regarding the student enrollments for a specific set of data i.e. the courses which the students like to learn. After collection of data, we apply three data mining techniques namely clustering, classification & association rule to find the best combination of courses. Here we compare the result of this combined approach with result obtained using only association rule & present how this combined approach is better than only the association rule algorithm. KEY WORDS Weka, Moodle, Simple K-means Algorithm, ADTree Classification Algorithm, Apriori Association Rule Algorithm 1. INTRODUCTION The course recommendation system in e-learning is a system that suggests the best combination of courses in which the students are interested [10]. In this Course Recommendation System, we have considered the 13 course category. Under each category there will courses. So there are about 82 courses. Student first logs in the learning management system e.g. Moodle & enrolled for those courses in which they are interested. The activity chart for student is shown in figure 2. This data is stored in the moodle database which we use to find out the best combination. After collecting the data from student which is stored in Moodle database, the next stage is to gather & prepare the data. In this step, first we select the data from database which is relevant. To test the result using Weka i.e. the best combination courses, first we need to preprocess the data & find out the result. The step-build the model, we directly select the relevant data from Moodle database. After collecting the data from Moodle database, we clustered the data using clustering algorithm e.g. Simple K-means algorithm. After clustering data, we classify that data using ADTree algorithm. We apply the Apriori Association Rule algorithm on classified data to find the best combination of courses. To find the result using only Apriori association rule algorithm, we need to preprocess the data from Moodle database but if we consider the combined approach then there is no need to preprocess the data. The preprocessing technique is explained in paper [8]. Figure 1: Architecture for recommendation of courses in E-learning System