Data mining model for higher education system

Abstract

Data mining is used to extract meaningful information and to develop significant relationships among variables stored in large data set/ data warehouse. In this paper data mining technique named k-means clustering is applied to analyze student's learning behavior. Here K-means clustering method is used to discover knowledge that come from educational environment. The students evaluation factors like class quizzes mid and final exam assignment are studied. It is recommended that all these correlated information should be conveyed to the class teacher before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students. © 2010 EuroJournals Publishing, Inc.