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Students' Admission Prediction using GRBST with Distributed Data Mining

Dineshkumar B Vaghela, Priyanka Sharma Published in Information Sciences

Communications on Applied Electronics
Year of Publication: 2015
© 2015 by CAE Journal
10.5120/cae-1643

Dineshkumar B Vaghela and Priyanka Sharma. Article: Students' Admission Prediction using GRBST with Distributed Data Mining. Communications on Applied Electronics 2(1):15-19, June 2015. Published by Foundation of Computer Science, New York, USA. BibTeX

@article{key:article,
	author = {Dineshkumar B Vaghela and Priyanka Sharma},
	title = {Article: Students' Admission Prediction using GRBST with Distributed Data Mining},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {2},
	number = {1},
	pages = {15-19},
	month = {June},
	note = {Published by Foundation of Computer Science, New York, USA}
}

Abstract

Data is the most important asset of any organization which is further processed to produce useful information. Data mining techniques are widely used for industrial sectors to generate the useful pattern helpful for earning more profits and expand business. Since last few years, lots of research works have been done by applying data mining techniques on educational data for improvement in Education System. Data Mining can be useful for predicting such as the students' admission, faculty performance, student performance, identifying the group of students of similar behavior. Very large educational institute's data are geographically spread and increase every year. It is very time consuming and tedious task of processing these large volume of data. In this paper, the new algorithm has been presented with Binary Search Tree which stores the global rules by consolidating the local rules generated at each site. This Global Rule Binary Search Tree (GRBST) can then be used in prediction of Students' admission to college.

Reference

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Keywords

Binary Search Tree, admission, prediction, distributed data mining, Education System (ES), Big Data, GRBST