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Efficient Clustering Technique for University Admission Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 45 - Number 23 |
| Year of Publication: 2012 |
| Authors: Abdul Fattah Mashat, Mohammed M. Fouad, Philip S. Yu, Tarek F. Gharib |
10.5120/7091-9796
|
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
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.
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