Babes-Bolyai University of Cluj-Napoca
Faculty of Mathematics and Computer Science
Study Cycle: Master

SUBJECT

Code
Subject
MII1010 Advanced Methods of Data Analysis
Section
Semester
Hours: C+S+L
Category
Type
Intelligent Systems - in English
1
2+1+0
speciality
compulsory
Formals Methods in Programming - in English
1
2+1+0
speciality
compulsory
Modeling and Simulation - in English
1
2+1+0
speciality
compulsory
Teaching Staff in Charge
Prof. POP Horia Florin, Ph.D.,  hfpopcs.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