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Efficient Clustering Technique for University Admission Data

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 45 - Number 23
Year of Publication: 2012
Abdul Fattah Mashat
Mohammed M. Fouad
Philip S. Yu
Tarek F. Gharib

Abdul Fattah Mashat, Mohammed M Fouad, Philip S Yu and Tarek F Gharib. Article: Efficient Clustering Technique for University Admission Data. International Journal of Computer Applications 45(23):39-42, May 2012. Full text available. BibTeX

	author = {Abdul Fattah Mashat and Mohammed M. Fouad and Philip S. Yu and Tarek F. Gharib},
	title = {Article: Efficient Clustering Technique for University Admission Data},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {45},
	number = {23},
	pages = {39-42},
	month = {May},
	note = {Full text available}


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.


  • S. Feng, S. Zhou and Y. Liu, (2011) "Research on Data Mining in University Admissions Decision-making", International Journal of Advancements Advancements in Computing Technology, vol. 3, no. 7, pp. 176-186.
  • J. Beck, (2007) "Difficulties in inferring student knowledge from observations (and why you should care)", Educational Data Mining workshop in conjunction with 13th International Conference of Artificial Intelligence in Education, Marina del Rey, CA. USA, pp. 21-30.
  • N. R. Pal, J. C. Bezdek and E. C. -K. Tsao, (1993) "Generalized clustering networks and Kohonen's self-organizing scheme", IEEE Trans. Neural Networks, vol. 4, no. 4, pp. 549-557.
  • J. Han and M. Kamber, (2000), Data mining:concepts and techniques, San Francisco, Morgan-Kaufma.
  • M. Verma, M. Srivastava, N. Chack, A. K. Diswar, N. Gupta, (2012) "A Comparative Study of Various Clustering Algorithms in Data Mining", International Journal of Engineering Research and Applications (IJERA), vol. 2, no. 3, pp. 1379-1384.
  • J. Hartigan and M. A. Wong, (1979) "A k-means clustering algorithm", Applied Statistics, vol. 28, pp. 100-108.
  • S. Wu and T. Chow, (2004) "Clustering of the self-organizing map using a clustering cvalidity index based on inter-cluster and intra-cluster density", Pattern Recognition, vol. 37, pp. 175-188.
  • J. C. Bezdec, (1971), Pattern Recognition with Fuzzy Objective Function Algorithms, New York, Plenum Press.
  • L. J. Deborah, R. Baskaran and A. Kannan, (2010) "A survey on Internal Validity Measure for Cluster Validation", International Journal of Computer Science & Engineering Surveys (IJCSES), vol. 1, no. 2, pp. 85-102.
  • M. J. A. Berry and G. Linoff, (1997), Data Mining Techniques: For Marketing, Sales, and Customer Support, Berlin, John Wiley & Sons.
  • D. Davies and D. Bouldin, (1979) "A Cluster Separation Measure", IEEE transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, pp. 2244-227.
  • M. Kim and R. S. Ramakrishna, (2005) "New indices for cluster validity assessment", Pattern Recogntion Letters, vol. 26, pp. 2353-2363.
  • K. R. Zalik and B, Zalik, (2011) "Validity index for clusters of different sizes and densities", Pattern Recognition Letters, 32, pp. 221-234.