Subject: Advanced Methods in Data Analysis	Code: MI371	

Semester	 Hours: C+S+L	Type	 Section	
1	2+2+1	compulsory	Modeling and Simulation  - in English	

Teaching Staff in Charge	Prof. POP Horia Florin, Ph.D., hfpop@cs.ubbcluj.ro	
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. Administrivia
2. Introduction to Data Mining	
3. Fuzzy sets; Rough sets
4. Fuzzy logic; Fuzzy reasoning
5. Fuzzy control systems
6. Decision trees; Association rules
7. Neural networks
8. Genetic algorithms
9. Methods for prediction
10. Principal components, Factor analysis
11. Classification; Clustering
12. Feature extraction; 
13. Performance analysis
14. Text mining, Web mining
15. Applications of data analysi	
Selective 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] Internet resource	
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 is computed as follows: 10% - Class attendance and participation; 30% - Two reports (written and presented on time); 30% - Software project (written, documented and demonstrated in time); 30% - Final exam (written paper in exams session). All elements are compulsory