Issue:Classification of the students' intuitionistic fuzzy estimations by a 3-dimensional self organizing map

shortcut

http://ifigenia.org/wiki/issue:nifs/17/4/39-44

Title of paper: Classification of the students' intuitionistic fuzzy estimations by a 3-dimensional self organizing map Author(s): Evdokia Sotirova University “Prof. Asen Zlatarov”, 1 “Yakimov” Blvd., Burgas 8010, Bulgaria esotirova@btu.bg Presented at: 7th IWIFS, Banska Bystrica, 27 September 2011 Published in: Conference proceedings, "Notes on IFS", Volume 17 (2011) Number 4, pages 39—44 Download:  PDF (85  Kb, File info) Abstract: The aim of the present paper is to use the techniques of self-organizing map (SOM) in the process of e-learning to assess the students’ knowledge on relevant topics in intuitionistic fuzzy form. The evaluation is formed on the basis of their answers. The self-organizing map is an effective tool for the visualization of high-dimensional data and its clustering. By clustering, students are classified into “similar” groups according to their intuitionistic fuzzy estimations. Thereby, a three-dimensional map for visualization of their knowledge in the intuitionistic fuzzy form is obtained. Keywords: Intuitionistic fuzzy sets, Self-organizing map, Clustering AMS Classification: 03E72, 91C20 References: Atanassov, K. Generalized Nets, World Scientific, Singapore, 1991. Atanassov, K. On Generalized Nets Theory, “Prof. M. Drinov”Academic Publishing House, Sofia, 2007. Atanassov K., Intuitionistic Fuzzy Sets: Theory and Applications, Springer Physica-Verlag, Berlin, 1999. Atanassov K., Intuitionistic fuzzy sets. Fuzzy Sets and Systems, Vol. 20, 1986, 87–96. Freeman, A. Neural Network, Algorithms Applications and Programming Techniques. Addison Wesley, Reading, MA, 1991. Glenn, J. Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. John Wiley, 2006. Reljin, I., B. Reljin, G. Jovanović, Clustering and mapping spatial-temporal datasets using SOM neural networks. Journal of Automatic Control, University of Belgrade; Vol. 13(1), 2003, 55–60. Kohonen, T. Self-Organizing Maps. Series in Information Sciences, Vol. 30. Springer, Heidelberg. 1995, Second ed. 1997. Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J. SOM_PAK: The self-organizing map program package. Report A31. Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland, 1996. Kaedi, M., M. Nematbakhsh, N. Ghasem-Aghaee. Fuzzy Association Rule Reduction Using Clustering In SOM Neural Network, Proc. of IADIS, European Conference Data Mining, 2008, 139–143. Mangiameli, P., S. K. Chen, D. West, A comparison of SOM neural network and hier¬archical clustering methods. Neural Networks and Operations Research/ Management Science, European Journal of Operational Research, Vol. 93, Issue 2, 1996, 402–417. Wasserman, P. D., Neural computing, Theory and Practice. Van Nostrand Reinhold, New York, 1989. Sotirova, E., T. Petkov, S. Surchev, M. Krawczak. Generalized net model of Clustering with Self Organizing Map, Developments in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics. Vol. 2: Applications Warsaw, Poland, 2011, 239–244. Sotirov, S., D. Orozova, E. Sotirova, Neural network for defining intuitionistic fuzzy sets in e-learning, Proc. of 13th Int. Conf. on Intuitionistic Fuzzy Sets, Sofia, NIFS Vol. 15, No. 2, 2009, 33–36. Sotirov, S., E. Sotirova, M. Krawczak, Application of data mining in digital university: multilayer perceptron for lecturer’s evaluation with intuitionistic fuzzy estimations, Issues in Intuitionistic Fuzzy Sets and Generalized Nets, Vol. 8, 2010, 102–107. Citations: The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.