Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Doctorat
Domeniul: Informatică
Programul de studii: Scoala doctorala
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MID2006	Capitole speciale de analiza datelor	

Semester	Hours: C+S+L+P	Category	Status	
1	2+1+0+1	specialty	compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
POP Horia Florin	Ph.D.	Prof.	Programming  Languages and Methods	*	*		

Associated faculty members
Name and	Scientific	Institution	Type of	Type of activity	
surname	title		position	C	S	L	

III. Course objectives
•	To introduce the student in advanced methods of data analysis
•	To present the field of intelligent data analysis as a novel research and application domain.
•	To induce the necessity of intelligent data analysis methods by studying some relevant practical applications
•	To offer the student the instruments that will allow him/her to develop different data analysis applications.	

IV. Course contents
Administrivia; Introduction to Data Mining; Fuzzy sets; Rough sets; Fuzzy logic; Fuzzy reasoning; Fuzzy control systems; Decision trees; Association rules; Neural networks; Genetic algorithms; Classification; Clustering; Principal components and Factor analysis; Feature extraction; Methods for prediction; Performance analysis; Text mining; Web mining; Applications of data analysis	

V. Bibliography
[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	

VI. Thematic of didactic activities per weeks
Schedule of courses

Week 1: Introduction
Reference: [Han, ch. 1], [Mitchell, ch. 1]
Week 2: Introduction to Fuzzy sets
Reference: [Klir, ch. 2, 3]
Week 3: Fuzzy logic, fuzzy reasoning
Reference: [Klir, ch. 8, 10]
Week 4: Fuzzy control systems
Reference: [Klir, ch. 12]
Week 5: Introduction to Rough sets
Reference: [Pawlak]
Week 6: Applications of Rough sets
Reference: [Ye, ch. 1], [5, ch. 3]
Week 7: Mining (Fuzzy) Association rules
Reference: [Ye, ch. 2]
Week 8, 9: (Fuzzy) Clustering
Reference: [Han, ch. 7], [Ye, ch. 10]
Week 10: Classification
Reference: [Han, ch. 6], [Mitchell, ch. 6], [Ye, ch. 1, 3]
Week 11: Linear and non-linear regression
Reference: [Ye, ch. 7]
Week 12: Principal components, Factor analysis
Reference: [Ye, ch. 8]
Week 13: Feature extraction, Performance analysis
Reference: [Ye, ch. 16, 17]
Week 14: Applications of data analysis
Reference: [Ye, ch. 21, 24, 27], [Han, ch. 10, 11]

Schedule of seminars

Week 1/2: Administration of seminar activities
Week 3/4: Fuzzy sets
	Fuzzy logic, fuzzy reasoning
	Fuzzy control systems
Week 5/6: Rough sets
Week 7/8: Association rules
	Classification and prediction
Week 9/10: Clustering
	Fuzzy Clustering
Week 11/12: Linear and non-linear regression
	Principal components, Factor analysis
Week 13/14: Presentation of semester projects	

VII. Didactic methods used
Expositions: description, explanation, class lectures, dialog-based lectures, lectures with demonstrations, introductive lectures, current lectures, synthesis lectures. 
Conversations: debate, dialog, introductive conversations, conversations for knowledge consolidation, conversations to systematize and synthesize knowledge 
Use of problems: use of problem questions, problems and problem situations. 
Discovery: directed and independent rediscovery, creative discovery, inductive discovery, deductive discovery, discovery by documenting, experimental discovery. 
Other methods: case study; cooperation, simulation, brainstorming, individual study, exercise, homework study.	

VIII. Assessment
The activity ends with a written final exam (grade E). During the semester, the students will have to prepare two theoretical reports (grades R1, R2) and a software project that will have to illustrate the use of intelligent data analysis methods to solve a specific task (grade P). The students’ activity during the semester will be also considered (grade A).  The final grade is the weighted mean of the five grades mentioned above. The final grade = 30%E + 20%R1 + 20%R2 + 20%P + 10%A. Successful passing of the exam is conditioned by the final grade that has to be at least 5. 
All university official rules with respect to students’ attendance of academic activities, as well as to cheating and plagiarism, are valid and enforced. Successful passing of the exam is conditioned by grade E to be at least 5.	

IX. 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	

	Dean,							Chair head,

	Date,							Course responsible,
	October 1, 2010					Prof. POP Horia Florin, Ph.D.