1. General Information 439 – Data Mining Assist.Prof.Dr. Derya BİRANT

2. General Information I ◘Instructor: Assist.Prof.Dr. Derya BİRANT –Email: derya@cs.deu.edu.tr –Tel: +90 (232) 412 74 18 ◘Course Code: 439 ◘Lecture Times: 13:15 – 16:00 Friday ◘Room: B7 ◘Office hours: Any time you want

3. General Information III ◘Course Web Page: http://cs.deu.edu.tr/~derya/datamining.htm http://cs.deu.edu.tr/~derya/datamining.htm Lecture slides will be made available on the course web page ◘Prerequisites: Database Systems Programming Skills

4. Instructor Info ◘8 years experience on Data Mining –PhD Thesis –Teaching Courses: CME4416 Introduction to Data Mining (2007-2010) (Undergraduate) CSE5072 Data Mining and Knowledge Discovery (2008-2010) (Master) CSE6003 Machine Learning (2008-2010) (Doctorate) –Projects Tübitak - Veri Madenciliği Çözümleri ile Yerel Yönetimlerde Bilgi Keşfi (2010-2011) Tübitak - NETSİS İş Zekası Çözümleri (2008 – 2009) BAP - Veri Madenciliğindeki Sınıflandırma Tekniklerinin Karşılaştırılması ve Örnek Uygulamalar (2009 - 2010) BAP - Büyük Konumsal-Zamansal Veritabanları için Veri Madenciliği Uygulamasının Geliştirilmesi (2007 - 2008) International project at SEE University (2006 – 2007) … –Supervisor of 4 Master Theses (related to Data Mining) –More than 12 publications (related to Data Mining) –…

5. Course Structure ◘The course has two parts: –Lectures Introduction to the main topics –Assignment and Project To be done in groups

6. Grading ◘Midterm Exam: ?% ◘Assignment and Project: ?% ◘Final Exam: ?%

7. Teaching materials ◘Text Book –Han, J. & Kamber, M., Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, 2 nd ed. 2006 ◘Reference Books –Roiger, R.J., & Geatz, M.W., Data Mining: A Tutorial-Based Primer, Addison Wesley, USA, 2003. –Dunham, M.H., Data Mining: Introductory and Advanced Topics, Prentice Hall, New Jersey, 2003.

8. Topics - I ◘WEEK 1. Data Mining: A First View What is Data Mining? Why Data Mining? History of Data Mining Data Mining Applications... ◘WEEK 2. Knowledge Discovery in Databases (KDD) Goal Identification Data Preparation o Data Integration o Data Selection o Data Preprocessing o Data Transformation Data Mining Presentation and Evaluation...

9. Topics - II ◘WEEK 3. Data Preparation Data Warehouses Data Preprocessing Techniques Data Integration Data Selection Data Preprocessing Data Transformation … ◘WEEK 4. Data Mining Techniques

10. Topics - III ◘WEEK 5. Association Rule Mining Mining Association Rules Support and Confidence ARM Algorithms Example Association Rule Mining Applications... ◘WEEK 6. Sequential Pattern Mining Mining Sequential Patterns SPM Algorithms Example Applications

11. Topics - IV ◘WEEK 7,8. Classification and Prediction Classification Methods: o Decision Trees o Bayesian Classification o Neural Network o Genetic Algorithms o Support Vector Machines (SVM) Example Classification Applications... ◘WEEK 9. Midterm Exam

12. Topics - V ◘WEEK 10, 11. Clustering Clustering Methods o Partitioning Clustering Methods o Density-Based Clustering Methods o Hierarchical Clustering Methods o Grid-Based Clustering Methods o Model-Based Clustering Methods Example Clustering Applications... ◘WEEK 12. Outlier Detection Outlier Detection Techniques Example Outlier Detection Applications

13. Topics - VI ◘WEEK 13. Web Mining Web Usage Mining Web Content Mining Web Structure Mining... ◘WEEK 14. Text Mining ◘WEEK 15. Data Mining Applications

14. Any questions and suggestions? ◘Your feedback is most welcome! –I need it to adapt the course to your needs. ◘Share your questions and concerns with the class – very likely others may have the same. ◘No pain no gain –The more you put in, the more you get –Your grades are proportional to your efforts.