syllabus
1. Information regarding the programme
1.1 Higher education institution 	Babeş Bolyai University	
1.2 Faculty	Faculty of Mathematics and Computer Science	
1.3 Department	Department of Computer Science	
1.4 Field of study	Computer Science	
1.5 Study cycle	Master	
1.6 Study programme / Qualification 	High Performance Computing  and Big Data Analytics - english	

2. Information regarding the discipline 
2.1 Name of the discipline	Data Mining	
2.2 Course coordinator 	Lect. Dr. Anca Andreica	
2.3 Seminar coordinator	Lect. Dr. Anca Andreica	
2.4. Year of study	2	2.5 Semester	1	2.6. Type of evaluation	E	2.7 Type of discipline	Optional	

3. Total estimated time (hours/semester of didactic activities) 
3.1 Hours per week 	4	Of which: 3.2 course	2	3.3 seminar/laboratory	1 lab+
1 project	
3.4 Total hours in the curriculum 	56	Of which: 3.5 course	28	3.6 seminar/laboratory	28	
Time allotment:	hours	
Learning using manual, course support, bibliography, course notes	40	
Additional documentation (in libraries, on electronic platforms, field documentation) 	40	
Preparation for seminars/labs, homework, papers, portfolios and essays	40	
Tutorship	10	
Evaluations	14	
Other activities: ..................		
3.7 Total individual study hours 	144	
3.8 Total hours per semester	200	
3.9 Number of ECTS credits	8	

4. Prerequisites (if necessary)
4.1. curriculum	•		
4.2. competencies	•	Average programming skills in a high level programming language	

5. Conditions (if necessary)
5.1. for the course	•		
5.2.  for the seminar /lab activities	•	Laboratory with computers	

6. Specific competencies acquired 
Professional competencies	•	Practical skills referring to modelling and solving real-world problems
•	Ability to work in teams, assuming different execution and leading roles, performing professional tasks with autonomy and responsibility	
Transversal competencies	
•	The students will learn various data analysis techniques, and will apply these techniques for solving data mining problems using special software systems and tools.	

7. Objectives of the discipline (outcome of the acquired competencies)
7.1 General objective of the discipline
	•	To learn data mining concepts, methods and techniques, from a database perspective.	
7.2 Specific objective of the discipline
	•	The students will learn various data analysis techniques, and will apply these techniques for solving data mining problems using special software systems and tools.
•	A perception of data mining as a strong application field, as well as a significant database research domain, will be formed.	

8. Content
8.1 Course	Teaching methods	Remarks	

1. Introduction; Types of data explored in data mining; Data mining functionalities; Patterns; Data mining from a database perspective; Data mining applications	
Exposure, description, explanation, examples, discussion of case studies		

2-3. Concept description; Definitions; Data generalization and summarization-based characterization; Analytical characterization: attribute relevance analysis; Class comparison: discriminating between classes; Descriptive statistical measures in large databases	
Exposure, description, explanation, examples, discussion of case studies		

4. Data preprocessing; Data cleaning; Data transformation and integration; Data reduction	
Exposure, description, explanation, examples, discussion of case studies		

5-6. Association rules; Problem definition; Algorithms for mining single-dimensional boolean association rules; Algorithms for mining multi-level association rules, multi-dimensional association rules, association rules with constraints; Correlation analysis	
Exposure, description, explanation, examples, discussion of case studies		

7-9. Classification and prediction; Problem definition;
  Classification using k-nearest neighbor method;
  Classification using decision tree induction;
  Classification and estimation using neural networks;
  Bayesian classification	
Exposure, description, explanation, examples, discussion of case studies		

10-11. Clustering (cluster analysis); Problem definition; Types of data in cluster analysis; Clustering methods; Outliers detection	
Exposure, description, explanation, examples, discussion of case studies		

12. Data mining standards and software
	
Exposure, description, explanation, examples, discussion of case studies		

13-14. Students theoretical reports presentations			
			
Bibliography

1. S. Chakrabarti et al, Data Mining. Know It All, Morgan Kaufmann, 2009.
2. K. Cios, W. Pedrycz, R. Swiniarski, L. Kurgan, Data Mining. A Knowledge Discovery Approach, Springer, 2007.
3. J. Han, M. Kamber, Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kaufmann, 2006.
4. P. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison Wesley, 2006.
5. D. Larose, Discovering Knowledge in Data. An Introduction to Data Mining, John Wiley & Sons, 2005.
6. Han, J., Kamber, M., Data Mining: Concepts and Techniques, 1st Edition, Morgan Kaufmann, 2000.
7. Weka system and documentation (http://www.cs.waikato.ac.nz/ml/weka/). Weka is a suite of machine learning / data mining software. It contains Java implementation for various mining algorithms, data preprocessing filters, and experimentation capabilities. Weka is free open-source software under the GNU General Public License (GPL).
	
8.2 Laboratory	Teaching methods	Remarks	

1. Data preprocessing
	
Practical projects		

2. Data Mining software tools

3. WEKA software			

4-6. Data mining applications			

7. Students project presentations			
			
Bibliography
	
1. S. Chakrabarti et al, Data Mining. Know It All, Morgan Kaufmann, 2009.
2. K. Cios, W. Pedrycz, R. Swiniarski, L. Kurgan, Data Mining. A Knowledge Discovery Approach, Springer, 2007.
3. J. Han, M. Kamber, Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kaufmann, 2006.
4. P. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison Wesley, 2006.
5. D. Larose, Discovering Knowledge in Data. An Introduction to Data Mining, John Wiley & Sons, 2005.
6. Han, J., Kamber, M., Data Mining: Concepts and Techniques, 1st Edition, Morgan Kaufmann, 2000.
7. Weka system and documentation (http://www.cs.waikato.ac.nz/ml/weka/). Weka is a suite of machine learning / data mining software. It contains Java implementation for various mining algorithms, data preprocessing filters, and experimentation capabilities. Weka is free open-source software under the GNU General Public License (GPL).

9. Corroborating the content of the discipline with the expectations of the epistemic community, professional associations and representative employers within the field of the program

•	The course exists in the studying program of all major universities in Romania and abroad;
•	The content of the course is considered the software companies as important for average programming skills

10. Evaluation
Type of activity	10.1 Evaluation criteria	10.2 Evaluation methods	10.3 Share in the grade (%)	
10.4 Course	- know the basic principle of the domain;
	Written exam	50%	
10.5 Lab activities	- data mining problems solving 	Laboratory work 	50%	
10.6 Minimum performance standards	

•	At least grade 5 at both written exam and laboratory work.	

Date				Signature of course coordinator 	Signature of seminar coordinator
23.02.2014			Lect. Dr. Anca Andreica		Lect. Dr. Anca Andreica

Date of approval  					  Signature of the head of department	
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