Prediction of Student Academic Performance by an Application of K-Means Clustering Algorithm
Data Clustering is used to extract meaningful information and to develop significant relationships among variables stored in large data set/data warehouse. In this paper data clustering technique named k-means clustering is applied to analyze student's learning behavior. The student's evaluation factor like class quizzes, mid and final exam assignment are studied. It is recommended that all these correlated information should be conveyed to the class advisor before the conduction of final exam. This paper will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students.