Knowledge discovery and data mining, data warehousing, data preparation and data mining primitives, concept description, mining association rules in large databases, classification and prediction, cluster analysis, web mining, applications in data mining.

Course Objectives

1.Read, abstarct and discuss research papers on one of the topics of computer science field (1), 2.Make a survey, propose a solution and test the performance of the solution on one of the open problems of the field (2, 3), 3.Document and present the research process (4)

Recommended or Required Reading

J. Han, Data Mining: Concepts and Techniques, Morgan Kaufman, 2000 ,M. H. Dunham, Data Mining Introductory and Advanced Topics, Prentice Hall, Pearson Education, 2003. ,R. J. Roiger and M. W. Geatz, Data Mining: A Tutorial Based Primer, Addison Wesley, 2003. ,M. J. A. Berry and G. S. Linoff, Mastering Data Mining Wiley, 2000.

Learning Outcomes

1. Recall the process of knowledge discovery and data mining algorithms and be able to compare them

2. Make a survey on one of the open problems of the field

3. Gain the ability of developing a knowledge discovery process using using data mining tools

4. Document and present the project of the developed knowledge discovery process

Grading

Midterm: 30%

Research Presentation: 40%

Final: 30%