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Implementation of Data Mining Techniques to Classify New Students into Their Classes: A Bayesian Approach

by Ramjeet Singh Yadav, A. K. Soni, Saurabh Pal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 85 - Number 11
Year of Publication: 2014
Authors: Ramjeet Singh Yadav, A. K. Soni, Saurabh Pal
10.5120/14885-3319

Ramjeet Singh Yadav, A. K. Soni, Saurabh Pal . Implementation of Data Mining Techniques to Classify New Students into Their Classes: A Bayesian Approach. International Journal of Computer Applications. 85, 11 ( January 2014), 16-19. DOI=10.5120/14885-3319

@article{ 10.5120/14885-3319,
author = { Ramjeet Singh Yadav, A. K. Soni, Saurabh Pal },
title = { Implementation of Data Mining Techniques to Classify New Students into Their Classes: A Bayesian Approach },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 85 },
number = { 11 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 16-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume85/number11/14885-3319/ },
doi = { 10.5120/14885-3319 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:02:10.027952+05:30
%A Ramjeet Singh Yadav
%A A. K. Soni
%A Saurabh Pal
%T Implementation of Data Mining Techniques to Classify New Students into Their Classes: A Bayesian Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 85
%N 11
%P 16-19
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In educational organizations the classification of new students into appropriate classes is a very challenging task presently. The smartest/intelligent students maybe clustered with the least intelligent in a same class. This problem may be solved by the use of Bayesian classification technique which considers the academic achievements of the students. In present research an attempt has been made to explore Bayesian classification to solve the allocation problem of new students. Based on the present study it is suggested that performance of Bayesian classification technique is more suitable compared to rest of techniques such as genetic algorithm method.

References
  1. Mankad, K. , Sajja, P. S. , and Akerkar, R. (2011). Evolving Rules Using Genetic Fuzzy Approach: An Educational Case Study. International Journal on Soft Computing, 2(1), 35-46.
  2. Zukhri, Z. , and Omar, K. (2008). Genetic algorithm with center based Chromosomal representation to solve new student allocation problem. Media Informatika, 5(2), 79-86.
  3. Everitt, B. S. , Landau, S. and Leese, M. (2001). Cluster Analysis. London: Heinemann Educational Books Ltd.
  4. Cole, R. M. (1998). Clustering with Genetic Algorithms. Master Thesis University of Western Australia.
  5. Wright, M. (2001). Experiments with a Plateau-Rich Solution Space. Proceedings of the 4th Meta Heuristics International Conference, 317-320.
  6. Susanto, S. , Suharto, I. and Sukapto, P. (2002). Using Fuzzy Clustering Algorithm for Allocation of Students. " Transaction on Engineering and Technology Education, 1(2), 245-248.
  7. Zukhri, Z. and Omar, K. (2006). Modification of Agglomerative Methods to Cluster New Students into Their Classes. Proceedings of the 1st International Conference on Mathematics and Statistics, 493-498.
  8. Zukhri, Z. , and Omar, K. (2007). Comparative Evaluation of Genetic Algorithm and Modification of Agglomerative Method in New Students Allocation Problem. Proceedings of Application of Information Technology National Seminar, B9-B12.
  9. Zukhri, Z. , and Omar, K. (2006). Implementation of Genetic Algorithms to Cluster New Students into Their Classes. Seminar Nasional Aplikasi Teknologi Informasi 2006 (SNATI 2006), 101-103.
Index Terms

Computer Science
Information Sciences

Keywords

Classification Bayesian Classification Prior Probability New Student Allocation Problem

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