Babes-Bolyai University of Cluj-Napoca
Faculty of Mathematics and Computer Science
Study Cycle: Master
SUBJECT
| MI371 | Advanced Methods in Data Analysis |
| 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 4. Fuzzy logic; Fuzzy reasoning 5. Fuzzy control systems 6. Rough sets; Decision tables 7. Decision trees; Association rules 8. Neural networks; 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 analysis |
| References |
|
[1] J. Han, M. Kamber, Data Mining: Concepts and Techniques, Academic Press, 2001
[2] G.J. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic, Prentice Hall, 1995 [3] T. Mitchell, Machine Learning, McGraw Hill, 1996 [4] Z. Pawlak, Rough Sets, Polish Academy of Sciences, Gliwice, 2004 [5] N. Ye, The Handbook of Data Mining, Lawrence Elbaum Associates Publishers, 2003 Additional 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] C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995 [4] J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Kluwer, 1981 [5] Y.H. Pao, Adaptive pattern recognition and neural networks, Addison Wesley, 1989 [6] Statsoft inc., Electronic Statistics Textbook, Tulsa, OK, 2004, http://www.statsoft.com [7] Internet resources |
| 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; 20% + 20% - Two reports (written and presented on time); 20% - Software project (written, documented and demonstrated in time); 30% - Final exam (written paper in exams session). All elements are compulsory. The course web page: http://www.cs.ubbcluj.ro/~hfpop/amda |
| Links: | Syllabus for all subjects Romanian version for this subject Rtf format for this subject |