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General Information
439 – Data Mining
Assist.Prof.Dr. Derya BİRANT
General Information I
◘ Instructor: Assist.Prof.Dr. Derya BİRANT – Email: [email protected]
– 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.