General Information

439 – Data Mining

Assist.Prof.Dr. Derya BİRANT

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

General Information III

◘ Course Web Page: http://cs.deu.edu.tr/~derya/datamining.htm

Lecture slides will be made available on the course web page

◘ Prerequisites: • Database Systems

• Programming Skills

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)

– …

Course Structure

◘ The course has two parts: – Lectures

• Introduction to the main topics

– Assignment and Project • To be done in groups

Grading

◘ Midterm Exam: ?%

◘ Assignment and Project: ?%

◘ Final Exam: ?%

Teaching materials

◘ Text Book– Han, J. & Kamber, M., Data Mining: Concepts and

Techniques, Morgan Kaufmann Publishers, San Francisco, 2nd 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.

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• ...

Topics - II

◘ WEEK 3. Data Preparation

• Data Warehouses• Data Preprocessing Techniques

• Data Integration

• Data Selection

• Data Preprocessing

• Data Transformation • …

◘ WEEK 4. Data Mining Techniques

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

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

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

Topics - VI

◘ WEEK 13. Web Mining

• Web Usage Mining• Web Content Mining• Web Structure Mining• ...

◘ WEEK 14. Text Mining

◘ WEEK 15. Data Mining Applications

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.