"Babes-Bolyai" University of Cluj-Napoca
Faculty of Mathematics and Computer Science
| Advanced methods in data analysis |
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| Teaching Staff in Charge |
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| Aims |
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To introduce the student in advanced methods of data analysis. To offer the student the instruments that will allow him/her to develop different data analysis applications. |
| Content |
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1. Fundamental concepts
2. Multivariate exploratory techiques o (clustering, factorial analysis, principal components analysis, clustering, multidimensional scaling, discriminant analysis) 3. Linear and nonlinear models o (linear models, nonlinear models, regression models, nonlinear regression) 4. Data mining, text mining 5. Fuzzy Logic and Rough Sets o (fuzzy sets, fuzzy logic, fuzzy systems, models based on fuzzy sets) 6. Methods based on neural networks o (multilayer neural networks, self organizing neural networks, etc) 7. Machine learning o (Bayesian methods, rules based methods, competitive learning) 8. Applications of data analysis |
| References |
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[1] A. Agresti, An Introduction to Categorical Data Analysis, Wiley, New York, 1996
[2] M. Barthold, D.J. Hand, Intelligent Data Analysis, Springer Verlag, 2003 [3] J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Kluwer, 1981 [4] C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995 [5] J. Han, M. Kamber, Data Mining: Concepts and Techniques, Academic Press, 2001 [6] G.J. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic, Prentice Hall, 1995 [7] Y.H. Pao, Adaptive pattern recognition and neural networks, Addison Wesley, 1989 [8] Statsoft inc., Electronic Statistics Textbook, Tulsa, OK, 2004, http://www.statsoft.com/textbook/stathome.html [9] Resurse Internet |
| Assessment |
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Each student has to prove that (s)he acquired an acceptable level of understanding and processing of the domain knowledge, that (s)he is able of expressing this knowledge in a coherent form, that (s)he has the ability to develop a conceptual analysis of the domain and to use the knowledge in problems solving. The final grade will be based on the following components: theoretical report (20%), applicative report (20%); semester project (20%); written paper (30%); class participation (10%). |