Efficient Clustering Technique for University Admission Data

Abdul Fattah Mashat; Mohammed M. Fouad; Philip S. Yu; Tarek F. Gharib

Research Article

Efficient Clustering Technique for University Admission Data

by Abdul Fattah Mashat, Mohammed M. Fouad, Philip S. Yu, Tarek F. Gharib

International Journal of Computer Applications

Foundation of Computer Science (FCS), NY, USA

Volume 45 - Issue 23

Published: May 2012

Authors: Abdul Fattah Mashat, Mohammed M. Fouad, Philip S. Yu, Tarek F. Gharib

10.5120/7091-9796

PDF

Abdul Fattah Mashat, Mohammed M. Fouad, Philip S. Yu, Tarek F. Gharib . Efficient Clustering Technique for University Admission Data. International Journal of Computer Applications. 45, 23 (May 2012), 39-42. DOI=10.5120/7091-9796

                        @article{ 10.5120/7091-9796,
                        author  = { Abdul Fattah Mashat,Mohammed M. Fouad,Philip S. Yu,Tarek F. Gharib },
                        title   = { Efficient Clustering Technique for University Admission Data },
                        journal = { International Journal of Computer Applications },
                        year    = { 2012 },
                        volume  = { 45 },
                        number  = { 23 },
                        pages   = { 39-42 },
                        doi     = { 10.5120/7091-9796 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }

                        %0 Journal Article
                        %D 2012
                        %A Abdul Fattah Mashat
                        %A Mohammed M. Fouad
                        %A Philip S. Yu
                        %A Tarek F. Gharib
                        %T Efficient Clustering Technique for University Admission Data%T 
                        %J International Journal of Computer Applications
                        %V 45
                        %N 23
                        %P 39-42
                        %R 10.5120/7091-9796
                        %I Foundation of Computer Science (FCS), NY, USA

Abstract

Educational Data Mining (EDM) is the process of converting raw data from educational systems to useful information that can be used by educational software developers, students, teachers, parents, and other educational researchers. In this paper, we present an efficient clustering technique for King Abdulaziz University (KAU) admission data. The model uses K-Means algorithm. The clustering quality is evaluated using the DB internal measure. Experimental results show that K-Means achieves the minimum DB value that gives the best fits natural partitions. Additional analysis is also presented from the perspective of university admission office.

References

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Index Terms

Computer Science

Information Sciences

No index terms available.

Keywords

Educational Data Mining (edm) Data Clustering University Admission Data Clustering Evaluation