Knowledge discovery overview, data mining in general, data preparation, basics of data mining, association rule mining, classification and prediction, cluster analysis, web mining, data mining applications, term projects.

Course Objectives

1.To enrich his technical background by meeting a new area,

2.To gain the skill of making research,

3.To be able to understand a complex problem and to be able to propose a solution,

4.To be able to manage a technical process,

5.To learn effective oral and written communication.

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. Learn the process of knowledge discovery and data mining algorithms

2. Gain the ability of using data mining tools

3. Understand a data mining problem, design the process overflow and implement the steps

4. Document and present a Project

Grading

Midterm: 30%

Presentation: 30%

Final: 40%