Advanced methods in data analysis 
ter 

Teaching Staff in Charge 

Aims 
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 
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 
[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 
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%). 