Abstract

Educational Data Mining deals with developing methods to explore unique types of data in educational settings by applying a combination of approaches such as data mining, statistical and machine learning to get viable information. The objective of this paper is to help prospective students in selecting good academy during their enrollment to degree courses. In this paper, an integrated approach consisting of evolutionary approach i.e. genetic algorithm for preprocessing the data of 75 academies of Bangalore and data mining approach i.e. k-means for clustering the academies is used. Thus the cluster obtained as result will consist of academies that will be ranked as Excellent [E], Good [G], Average [A] and [Poor] according to the considered attributes. This work will help the prospective students in selecting the best academy during admission.

References

• Arora et al., ‘Admission Management through Data Mining using WEKA’ International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, pp. 674-678, October 2013
• Arora et al., ‘Location wise Student Admission Analysis’, International Journal of Computer Science and Information Technology & Security (IJCSITS), ,Vol. 2, No.6, ISSN: 2249-9555, December 2012
• Arora et al. ‘subject distribution using data mining’ International Journal of Research in Engineering and Technology Volume (IJRET) 02, Issue: 12 , eISSN: 2319-1163 | ISSN: 2321-7308, Dec-2013,
• M. Sukanya, S. Biruntha, Dr.S. Karthik, T. Kalaikumaran ‘Improving the Performance in Education Sector using Data Mining Techniques’ Data mining and knowledge engineering, Vol 4, No 8 (2012).
• JayanthiRanjan, Kamna Malik, "Effective educational process: a data-mining approach" ,VINE, Vol. 37 Iss: 4, pp.502 – 515, 2007
• Behrouz Minaei-Bidgoli, William F. Punch, ‘Using Genetic Algorithms for Data Mining Optimization in an Educational Web-Based System’, Genetic and Evolutionary Computation Conference Chicago, Volume 2724, pp 2252-2263, 2003
• Behrouz Minaei-Bidgoli , Deborah A. Kashy , GerdKortemeyer , William F. Punch ‘predicting student performance: an application of data mining methods with the educational web-based system LON-CAPA’, 33rd ASEE/IEEE Frontiers in Education Conference, 2003
• Mu-Jung Huang a, Hwa-Shan Huang a, Mu-Yen Chen ‘Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach’ Expert Systems with Applications (2006)
• AzinKhosravi Khorashad1, KianooshZakerHaghighy, ‘Application of Genetic Algorithm in Regional Planning’, J. Basic. Appl. Sci. Res., 2(11)11428-11433, 2012
• Sandeep singhrawat, lakshmirajamani ‘Timetable prediction for technical educational system using genetic algorithm’, Journal of Theoretical and Applied Information Technology, 2010
• AlirezaArabasadi et al., ‘A New Hybrid Algorithm for Traveler Salesman Problem based on Genetic Algorithms and Artificial Neural Networks’ International Journal of Computer Applications (0975 – 8887) Volume 24– No.5, June 2011
• Krishna.k, Murty M.N “Genetic k-means algorithm”, volume 29, issue 3, 1999 pages 435-439.
• Zhexue Huang “Extensions to the k-means algorithm for clustering large data sets with categorical values”, volume 2, issue 3, pages 283-304, 1998.
• Leon Bottou, YoshuaBengio “Convergence properties of the k-means algorithms”, 1995.