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Two Phase Iterative Clustering for Educational Data

by S. M. Karad, Prasad S. Halgaonkar, V. M. Wadhai, Dipti D. Patil, M. U. Kharat
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 5
Year of Publication: 2012
Authors: S. M. Karad, Prasad S. Halgaonkar, V. M. Wadhai, Dipti D. Patil, M. U. Kharat
10.5120/ijais12-450173

S. M. Karad, Prasad S. Halgaonkar, V. M. Wadhai, Dipti D. Patil, M. U. Kharat . Two Phase Iterative Clustering for Educational Data. International Journal of Applied Information Systems. 1, 5 ( February 2012), 11-15. DOI=10.5120/ijais12-450173

@article{ 10.5120/ijais12-450173,
author = { S. M. Karad, Prasad S. Halgaonkar, V. M. Wadhai, Dipti D. Patil, M. U. Kharat },
title = { Two Phase Iterative Clustering for Educational Data },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2012 },
volume = { 1 },
number = { 5 },
month = { February },
year = { 2012 },
issn = { 2249-0868 },
pages = { 11-15 },
numpages = {9},
url = { https://www.ijais.org/archives/volume1/number5/88-0173/ },
doi = { 10.5120/ijais12-450173 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:41:27.569404+05:30
%A S. M. Karad
%A Prasad S. Halgaonkar
%A V. M. Wadhai
%A Dipti D. Patil
%A M. U. Kharat
%T Two Phase Iterative Clustering for Educational Data
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 1
%N 5
%P 11-15
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the field of data mining, clustering of educational data has not given much of the importance. Considering the growth of educational field as a business, clustering of educational data must be focused as it can give effective results as in the case of mining enrolled students on the basis of education they undertake. A new algorithm is proposed and implemented by us for clustering educational data. This algorithm is based on a continuous looping procedure. Raw dataset is assigned to clustering algorithm initially and a novel cluster is identified for partition whose cluster high degree is less. Then improvement of degree of cluster is carried out. In this algorithm on the basis of homogeneity, cluster high degree is defined. Experiment is carried out on educational data; which provides good high degree clusters.

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Index Terms

Computer Science
Information Sciences

Keywords

Clustering Cluster homogeneity Educational Data

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