ISSN

2231-3915 (Online)
2231-3907 (Print)


Author(s): Bhawna Janghel, Asha Ambhaikar

Email(s): sjanghel2012@gmail.com , dr.asha.ambhaikar@gmail.com

DOI: 10.5958/2231-3915.2020.00011.5   

Address: Mrs. Bhawna Janghel, Dr. Asha Ambhaikar
Department of Computer Science, Kalinga university, Raipur, India.
*Corresponding Author

Published In:   Volume - 10,      Issue - 1,     Year - 2020


ABSTRACT:
Data clustering is the process of grouping a set of objects that objects is the same group are more similar to each other than to those in other groups. In this Paper Clustering is used as K-mean clustering to evaluate student performance based on their result of quarterly exam, half yearly exam, and final exams result. On the basis of academics performance we can compare the result of govt. school vs private school, this will help us to find out better education system.


Cite this article:
Bhawna Janghel, Asha Ambhaikar. Performance of Student Academics By K-Mean Clustering Algorithm. Int. J. Tech. 2020; 10(1):58-61. doi: 10.5958/2231-3915.2020.00011.5

Cite(Electronic):
Bhawna Janghel, Asha Ambhaikar. Performance of Student Academics By K-Mean Clustering Algorithm. Int. J. Tech. 2020; 10(1):58-61. doi: 10.5958/2231-3915.2020.00011.5   Available on: https://www.ijtonline.com/AbstractView.aspx?PID=2020-10-1-11


REFERENCES:
1.    Datamining Tutorial, Home>BigDataandAnalytics>Datamining, http://www.tutorialride.com>datamining.
2.    https://sites.google.com/site/dataclusteringalgorithms/k-means-clustering-algorithm
3.    Oyelade, O. J, Oladipupo, O.O, Obagbuwa.I.C (IJCSIS), Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance, Vol. 7, _o. 1, 2010,
4.    Sunita B Aher, Mr. LOBO L.M.R.J., Data Mining in Educational System using WEKA, (ICETT) 2011
5.    Bindiya M Varghese, Jose Tomy J, Unnikrishnan A, Poulose Jacob K, Clustering Student Data to Characterize Performance Patterns, (IJACSA)
6.    Mohammed M. Abu Tair, Alaa M. El-Halees, Mining Educational Data to Improve Students’ Performance: Volume 2 No. 2, February 2012
7.    Dorina Kabakchieva, Student Performance Prediction by Using Data Mining Classification Algorithms, Vol 1 Issue 4 November 2012
8.    Surjeet Kumar Yadav, Saurabh Pal, Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification, Vol. 2, No. 2, 51-56, 2012
9.    Dr. Sudhir B. Jagtap, Dr. Kodge B. G., Census Data Mining and Data Analysis using WEKA, (ICETSTM – 2013)
10.    P.Veeramuthu, Dr.R.Periyasamy, V.Sugasini, Analysis of Student Result Using Clustering Techniques, (IJCSIT) Vol. 5 (4), 2014
11.    M. Durairaj, C. Vijitha, Educational Data mining for Prediction of Student Performance Using Clustering Algorithms, (IJCSIT) Vol. 5 (4), 2014
12.    Kashish Kohli, Shiivong Birla, Data Mining on Student Database to Improve Future Performance, 15, July 2016
13.    Mr. Shashikant Pradip Borgavakar, Mr. Amit Shrivastava, Evaluating Student’s Performance using K-Means Clustering, Vol. 6 Issue 05, May – 2017
14.    Dr. K. Karthikeyan, P. Kavipriya, On Improving Student Performance Prediction in Education Systems using Enhanced Data Mining Techniques, Volume 7, Issue 5, May 2017
15.    Hilal Almarabeh, Analysis of Students' Performance by Using Different Data Mining Classifiers, 2017.08.02
16.    Hafez Mousa1, Ashraf Maghari2, School Students' performance Predication Using Data Mining Classification, Vol. 6, Issue 8, August 2017
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18.    Abdelbaset Al-Masri, Experiences in Mining Educational Data to Analyze Teacher's Performance: A Case Study with High Educational Teachers, 2017.10.12.01

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