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 the 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. However, other related information to data mining, relevant for the KDD process, is also presented: data warehouses, OLAP, data preprocessing. 
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
1. Introduction  
  Data mining - what is it, what are the factors that favoured this domain development, data mining and KDD (Knowledge Discovery in Databases) process
  Types of data explored in data mining 
  Data mining functionalities
  Patterns and interesting patterns
  Data mining from a database perspective
2. Data warehouses and OLAP tehnology - overview 
  What are data warehouses
  A multidimensional data model
  Data warehouse architecture
  Data warehouse implementation
  From data warehouses to data mining
3. Concept description - characterization and comparison 
  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 
  Discretization and concept hierarchy generation 
5. Mining association rules (associations analysis)
  Problem definition
  Algorithms for mining single-dimensional boolean association rules from  transaction databases - Apriori, FP-Growth
  Algorithms for mining multi-level association rules, multi-dimensional association rules, association rules with constraints
  Correlation analysis 
  ODM and association analysis in ODM 
6. Classification and prediction 
  Problem definition
  Classification using decision tree induction 
  Bayes classification
  Other classification methods
  Prediction - linear regression
  Classifier accuracy 
  ODM and classification in ODM 
7. Clustering (cluster analysis) 
  Problem definition
  Types of data in cluster analysis
  Clustering methods classification 
  Clustering methods classes: partitioning, hierarchical, density-based, grid- based, model-based clustering methods
  Outliers detection
  ODM and cluster analysis in ODM 
8. Data mining standards and software - ODM, Microsoft OLE DB  
9. Applications and trends in data mining 
  Applications: telecommunications, financial data analysis, biological data analysis, etc.
  Data mining in statistical, audio, video databases
  Data mining, data security and privacy

V. Bibliography
1. Han, J., Kamber, M., Data Mining: Concepts and Techniques, 1st Edition, Morgan Kaufmann, 2000.
2. ODM (Oracle Data Mining) Documentation (electronic format).
3. P. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison Wesley, 2006. 
4. P. Adriaans, D. Zantinge, Data Mining, Addison-Wesley, 1996.
5. Conference and journal papers (provided by the instructor).
6. 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

VII. Didactic methods used

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 several practical (lab) projects, consisting in implementing data mining (association analysis, classification, cluster analysis) 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 + 25%R + 35%P. The students who will show considerable research abilities, involving into projects development and research results publication will be granted additional 10% score to the final grade. In order to successfully pass the exam, the final grade has to be at least 5.	

IX. Additional references

	Dean,							Chair head,

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