Unveiling Hidden Dependencies with Rough Sets Methods
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2015 by IJCTT Journal | ||
| Volume-24 Number-1 |
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| Year of Publication : 2015 | ||
| Authors : Sylvia Encheva | ||
| DOI : 10.14445/22312803/IJCTT-V24P108 |
Sylvia Encheva "Unveiling Hidden Dependencies with Rough Sets Methods". International Journal of Computer Trends and Technology (IJCTT) V24(1):41-44, June 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Many researchers consider various ways for
early detection of students who are likely to experience
serious difficulties in their studies. Some of them focus
anxiety related problems connected with exam and general
performance, while other concentrate on particular
subjects’ associated ones. Mathematical subjects appear to
be among the ones causing problems for engineering
students. Some of these problems are related to thinking
logically, communicating mathematical arguments and
conclusions, understanding abstract concepts as well as
overcoming technical difficulties encountered when
studying new topics. In this work we apply methods from
rough sets theory for drawing conclusions from
inconsistent datasets obtained from students’ tests results.
Decision rules are visually represented with flow graphs.
References
[1] I. Duntsch and G. Gediga, Rough set data analysis: A road to
non-invasive knowledge discovery, Methodos Publishers,
2000.
[2] A. M. Bhavitha and S. Madhuri, A Classification Method
using Decision Tree for Uncertain Data, International
Journal of Computer Trends and Technology, 3(1), pp. 102-
107, 2012
[3] A. Johnstone, Effective Practice in Objective Assessment,
www.physsci.heacademy.ac.uk/Publications/PracticeGuide/
[4] J. Kacprzyk and M. Roubens, Non Conventional Preference
Relations in Decision Making, Springer Verlag, LNMES, 301,
Berlin, 1988.
[5] G. A. Klein, Recognition-Primed Decision Making. In Klein,
G. A. (Ed), Sources of power: How people make decisions,
Cambridge, MA: MIT Press, pp. 15–30, 1998.
[6] H. Liu and R. Setiono, A probabilistic approach to feature
search selection - A filter solution. In: Kumar, S. (Ed.), 13th
International Conference on Machine Learning (ICML’96),
Bari, Italy, pp. 319–327, 1996.
[7] M. Inuiguchi, S. Tsumoto, and S. Hirano, Rough Set Theory
and Granular Computing, Springer-Verlag Berlin and
Heidelberg GmbH & Co. KG, 2010.
[8] Z. Pawlak, Rough Sets, International Journal of Computer
and Information Sciences, 11, pp. 341–356, 1982.
[9] Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning
about Data, vol. 9 Kluwer Academic Publishers, Dordrecht,
The Netherlands, 1991.
[10] Z. Pawlak, Rough sets, decision algorithms and Bayes’
theorem, European Journal of Operational Research, 136, pp.
181–189, 2002.
[11] Z. Pawlak, In Pursuit of Patterns in Data Reasoning from
Data - The Rough Set Way, in J.J. Alpigini et al. (Eds.)
RSCTC 2002, LNAI 2475, Springer-Verlag Berlin
Heidelberg, pp. 1–9, 2002.
[12] P. Ramasubramanian, K. Iyakutti, P. Thangavelu, and J. J.
Winston, Teaching result analysis using rough sets and data
mining, Journal of Computing, 1(1), pp. 168–174, 2009.
[13] P. Ramasubramanian, K. Iyakutti, and P. Thangavelu,
Enhanced data mining analysis in higher educational system
using rough set theory, African Journal of Mathematics and
Computer Science Research, 2(9), pp. 184–188, 2009.
[14] P. Ramasubramanian, K. Iyakutti, and P. Thangavelu,
Ranking of Professional Colleges using Rough Sets and Data
Mining, Journal of Advanced Research in Computer
Engineering, 3(2), pp. 421-424, 2009.
[15] P. Ramasubramanian, S. B. Priya, and T. Dhanalakshmi,
Study on Assessment Method for Technical Institutions
Using Rough Sets, International Journal of Research and
Reviews in Information Sciences (IJRRIS), 1(4), 2011.
[16] R. W. Swiniarski and A. Skowron, Rough set methods in
feature selection and recognition, Pattern Recognition Letters,
24, pp. 833–849, 2003.
[17] Q. Zhiming and W. Xiaoli, Application of Clustering
Algorithm and Rough Set in Distance Education, First
International Workshop on Education Technology and
Computer Science, 2009. ETCS ’09, Wuhan, Hubei, pp.
489–493, 2009.
Keywords
Decision making, rough sets, inconsistent
data, learning.