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