Paper Title
An Optimised Approach For Student’s Academic Performance By K-Means Clustering Algorithm Using WEKA Interface

One of the significant facts in higher learning institution is the explosive growth educational database. These databases are rapidly increasing without any benefit to manage the database. The Clustering techniques have a wide use and importance now- a- days and this importance tends to increase as the amount of data grows. In this paper K-means clustering technique is applied to analyse student academic performance. This study makes use of cluster analysis to segment students into groups according to their characteristics. This include the students evaluation factors like class internal marks, GPA, mid and final exam, assignment, lab–work are studied. It is recommended that all these correlated information should be conveyed to the class teacher before the conduction of final exam. This research will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students. This paper presents an optimal procedure based on K- Means Clustering algorithm using Weka Interface that enables academicians to enhance the student’s education quality and the instructor can take necessary steps to improve student academic performance based on it. It also includes detailed result analysis of student performance data record after demonstration via Weka Interface.