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General Information

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 C ode : 439 Lecture T imes: 13:15 – 16:00 Friday Room: B7

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General Information

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  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) 41274 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 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, 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.

  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 MiningTechniques

  10. Topics - III • WEEK 5. Association Rule Mining • Mining Association Rules • SupportandConfidence • ARM Algorithms • Example Association Rule Mining Applications• ... • WEEK 6. Sequential Pattern Mining • Mining SequentialPatterns • SPM Algorithms • Example Applications

  11. Topics - IV • WEEK 7,8. Classification and Prediction • Classification Methods: o Decision Treeso Bayesian Classificationo Neural Network o Genetic Algorithms o Support Vector Machines (SVM) • Example Classification Applications• ... • WEEK 9. MidtermExam

  12. Topics - V • WEEK 10, 11. Clustering • Clustering Methodso Partitioning Clustering Methodso Density-Based Clustering Methods o Hierarchical Clustering Methodso Grid-Based Clustering Methodso 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.

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