Student Interest Group Prediction using Clustering Analysis: An EDM approach

This paper proposes a clustering-based approach to identify and predict a suitable interest group for students in higher education system. Student interest group stands for on-campus students club that reflects the co-curricular or extra-curricular participation of students apart from general academics. Such interest groups play a vital role in development of a student’s overall personality. K-means clustering algorithm has been used for this purpose. The experiment has been carried out on data collected by surveying students in higher education space. The purpose of this survey is to capture interest features of the student which is fed to the clustering algorithm. The overall concept ensures streamlining of the student’s efforts to maximize success.