Case Study: Data Mining of Associate Degree Accepted Candidates by Modular Method

Behrouz Minaei Bidgoli, Maryam Nazaridoust

Department of Computer Engineering and Information Technology, University of Qom, Qom, Iran.
Department of Computer Engineering, University of Science and Technology, Tehran, Iran.

DOI: 10.4236/cn.2012.43030                

Abstract

Since about 10 years ago, University of Applied Science and Technology (UAST) in Iran has admitted students in discontinuous associate degree by modular method, so that almost 100,000 students are accepted every year. Although the first aim of holding such courses was to improve scientific and skill level of employees, over time a considerable group of unemployed people have been interested to participate in these courses. According to this fact, in this paper, we mine and analyze a sample data of accepted candidates in modular 2008 and 2009 courses by using unsupervised and supervised learning paradigms. In the first step, by using unsupervised paradigm, we grouped (clustered) set of modular accepted candidates based on their student status and labeled data sets by three classes so that each class somehow shows educational and student status of modular accepted candidates. In the second step, by using supervised and unsupervised algorithms, we generated predicting models in 2008 data sets. Then, by making a comparison between performances of generated models, we selected predicting model of association rules through which some rules were extracted. Finally, this model is executed for Test set which includes accepted candidates of next course then by evaluation of results, the percentage of correctness and confidentiality of obtained results can be viewed.

Keywords

Supervised Learning; Unsupervised Learning; Clustering; Clustering Similarity; Labeling; Association Rule; Decision Tree; Predict Model; Predicting Majors

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	http://www.uast.ac.ir/en/default.aspx
[2]	M. Qazanfari and S. Alizadeh, “Data Mining and Knowledge Discovery,” University of Science and Technology, 2008.
[3]	C. Romero and S. Ventura, “Educational Data Mining: A Survey from 1995 to 2005,” Expert Systems with Applications, Vol. 33, No. 1, 2007, pp. 135-146. doi:10.1016/j.eswa.2006.04.005
[4]	J. Shahrabi and V. S. Niaz, “Data Mining in SQL Server,” Jahad Daneshgahi, Inc., Tehran, 2009.
[5]	P. N. Tan, M. Steinbach and V. Kumar, “Introduction to Data Mining,” Addison Wesley, Pearson, 2006.
[6]	E. Bae, J. Bailey and G. Z. Dong, “A Clustering Comparison Measure Using Density Profiles,” Springer, 2010.
[7]	S. Jamal and S. Ali Zolqadr, “Advanced Data Mining Concepts & Algorithms,” Jahad Daneshgahi, Inc., Tehran, 2009.
[8]	Spss Inc., “Clementine?12 Algorithms Guide,” United State of America, 2007, pp. 80-81.
[9]	Z. H. Tang and J. MacLennan, “Data Mining with SQL SERVER2005,” Microsoft Decision Tree, Wiley publishing, Indianapolis, 2004.
[10]	Oracle, “Oracle Data Mining Concept 11g,” About Classification, Inc., 2007.
[11]	SSAS, “Data Mining Tutorials,” Microsoft SQL Server 2008 Analysis Services (SSAS), Microsoft Corporation, Inc., 2008.
[12]	SQL Server 2008 Books Online, “Testing Accuracy with Lift Charts,” Microsoft Corporation, 2008.