Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Masterat
Domeniul: Informatica
Programul de studii: Programare bazată pe componente - în limba engleză
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MIH1005	Data Mining	

Semester	Hours: C+S+L+P	Category	Status	
3	2+1+0+1	speciality	optional	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
GOG Anca-Mirela	Ph.D.	Lect.	Computer Systems	*			

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

III. Course objectives
The background that favored data mining extensive development was the wide availability of huge amounts of data, coupled with the ever-increasing computational power that allowed these data sets to be analyzed. These data, adequately explored and analyzed, can be turned into useful information and knowledge, in different areas and for various applications: decision making, process control, production control, business management, market analysis, science exploration, information management, query processing etc. 
This course presents recent developments in knowledge discovery in databases domain (KDD), with focus on an essential step in the KDD process, the data mining step.
The course introduces data mining concepts, methods and techniques, from a database perspective. The focus is on different data mining problems (tasks) and their corresponding solutions. 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.	

IV. Course contents
Introduction; Concept description; Data preprocessing; Association rules; Classification and prediction; Cluster analysis; Data mining standards and software.	

V. 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).	

VI. Thematic of didactic activities per weeks
Course
1. Introduction  
  Types of data explored in data mining 
  Data mining functionalities
  Patterns
  Data mining from a database perspective
  Data mining applications
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
4. Data preprocessing 
  Data cleaning
  Data transformation and integration 
  Data reduction 
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 
 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
10-11. Clustering (cluster analysis) 
  Problem definition
  Types of data in cluster analysis
  Clustering methods
  Outliers detection
12. Data mining standards and software
13-14. Students theoretical reports presentations

Seminary
1. Data preprocessing
2. Data Mining software tools
3-4. WEKA software
5-6. Data mining applications 
7. Students project presentations

Project
Practical project consisting of implementing data mining (association analysis, classification, cluster analysis etc.) algorithms and performing data analysis using specialized software tools.	

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 exam (grade E). During the semester, the students will prepare and present a theoretical report (grade R) and a practical project, consisting of implementing data mining (association analysis, classification, cluster analysis etc.) algorithms and performing data analysis using specialized software tools (grade P). The final grade is a weighted mean of the three grades mentioned above: Final Grade = 40%E + 20%R + 40%P. The students who will show considerable research abilities, involving into projects development and research results publication will be granted 1 additional point to the final grade. In order to successfully pass the exam, the final grade has to be at least 5.
The required details for passing this exam can be found at http://www.cs.ubbcluj.ro/~anca/dm/	

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
								Lect. GOG Anca-Mirela, Ph.D.