Enhancing Academic Decision making at Higher Educational Institutions using Classification and Clustering Techniques

Alok Singh Chauhan¹

1. Department of Information Technology, IMS, Ghaziabad (University Courses Campus), UP, INDIA.

Correspondence should be addressed to: alok.chauhan@imsuc.ac.in .

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.2 , pp.39-44, Apr-2020

Online published on Apr 30, 2020

Copyright © Alok Singh Chauhan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The main objective of higher education institutions is to provide quality education to its students. Student`s performance prediction is essential to be conducted for a university to prevent student fail. This paper represents the data mining techniques used for analysing student’s performance. Educational institutions contain an enormous amount of academic database containing student details. These student databases along with other attributes are taken into consideration like family background, family income, etc. It will help us by identifying promising students and by providing us a chance to pay heed and to refine those students who likely get low marks. For answer, we prepare a structure which will analyse the student’s performance from their last performances using concepts of Data Mining under Classification and Clustering techniques like Decision Tree, Neural Network and K-Means Clustering Algorithm. By these techniques we extract knowledge that describes students’ performance in end semester examination.

Key-Words / Index Term :
Classification, Clustering Decision Tree, and Neural Network

References :
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