1. Sotarat Thammaboosadee, Ph.D. EGIT563- Data Mining Course Outline

2. Instructors Sotarat Thammaboosadee, Ph.D. Office: R308 Line: zotarutto e-mail: zotarat@gmail.com Office hours: Saturday afternoon / kindly appoint Course website: http://datamining.zotarat.com/http://datamining.zotarat.com/ About instructor: http://zotarat.comhttp://zotarat.com

3. Textbooks Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, 2005 Introduction to Data Mining Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) by Ian H. Witten and Eibe Frank. Morgan Kaufmann, 2005. Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) Discovering Knowledge in Data: An Introduction to Data Mining by Daniel T. Larose, 2004 Discovering Knowledge in Data: An Introduction to Data Mining

4. Tentative Course Schedule

5. Resources Weka: http://www.cs.waikato.ac.nz/~ml/weka/index.html http://www.cs.waikato.ac.nz/~ml/weka/index.html UC Irvine Machine Learning Repository: http://archive.ics.uci.edu/ml/ http://archive.ics.uci.edu/ml/

6. Scoring Midterm Exam30% Final Exam30% Assignments15% Project15% Research Paper10%

7. Homework Each HW, standard 2-3 points on each Average plus 0 point Goodplus 1 point Poorminus 1 point Copy0 point Submit 1 week after assigning Send to zotarat@gmail.comzotarat@gmail.com Email subject: HW1-5737xxx Send in PDF format

8. Project Use WEKA in existing dataset Perform complete loop of data mining tasks Submit a report in PDF file before the final examination date Present on 15 th week. Contents Problems and nature of selected data Give details on each selected method on each task Give detail of experimental results May compare between multiple methods

9. Research Paper Instructor will assign research paper to student midterm exam week. Student must present their assigned paper on 16 th week.

10. Attendance No scoring of attendance for graduated students

11. Tools WEKA http://www.cs.waikato.ac.nz/~ml/weka/index.html RapidMiner http://rapidminer.com/ KNIME http://www.knime.org/

12. About Instructor

13. Advisee’s publications (Data Mining selected) CRM Strategies discovered by Clustering Technique and Business Intelligence; case study in Chemical Industry A Truck Tires Usage Worthiness Prediction Model Constructing a Risk Behavior Guideline for Adolescent Students using Decision Tree The Data Mining Applications of Shoulder Pain Patients Treatment: Physical Therapy Equipment Usage Approaches Gold Price Volatility Prediction by Text Mining in Economic Indicators News Discovering Association between Metabolic Syndrome and Its Related Chronic Diseases Represented by ICD-10 Code Applying Data Mining Techniques and extended RFM Model in Customer Loyalty Measurement Air Quality Classification in Thailand Based on Decision Tree A Lexiconizing Framework of Feature-based Opinion Mining in Tourism Industry

14. Fields of Interests Health Informatics Legal Informatics and Computational Law Knowledge Discovery in Criminal Law Social Informatics Social Fraud Analysis Social Networking & Social Computing Information Technology Law and Policy Information Technology Valuation Data Science and Big Data Technology Knowledge Discovery and Data Mining Database and Data Warehousing Enterprise Database Indexing & Tuning Information Retrieval Decision Support Systems Software Engineering