1. 1 SBM411 資料探勘 陳春賢

2. 2 Lecture I Class Introduction

3. 3 Instructor Information  姓名 : 陳春賢  Ph.D. from Iowa State University, USA  M.S. from Iowa State University, USA  B.E. from 新竹清華大學  Technical specialty: Databases and Intelligent Decision Support Systems.  Research interests: Data Mining, Biomedical informatics, Artificial Intelligence, Artificial Neural Networks

4. 4 Contact Info  Contact Info: TEL: (03)211-8800 ext 5816 Email: cchen@mail.cgu.edu.tw

5. 5 Course Objectives To learn  The terms, concepts and applications of data mining  The processes, techniques and models of data mining  Data preprocessing techniques  Data Warehouse and OLAP technology  To use free data mining software: Weka to analyze a certain data set

6. 6 Course Content  Introduction to data mining  Main data mining techniques Association rule mining Classification and prediction Cluster analysis  Open source DM software in Java: Weka 3.x  Data preprocessing techniques  Data warehouse and OLAP Technology

7. 7 Textbook and References  Textbook Jiawei Han and Micheline Kamber, Data Mining : Concepts and Techniques, 2nd edition, Morgan Kaufmann Publishers, San Francisco, CA, USA, 2007.  參考書 Margaret H. Dunham, Data Mining: Introductory and Advanced Topics, Prentice Hall, Upper Saddle River, NJ, USA, 2002. 王派洲 譯，資料探勘 : 概念與方法，第二版 (Jiawei Han and Micheline Kamber, Data Mining:Concepts and Techniques,2/e) ， 滄海書局， 2008.

8. 8 Grading Policy  10% : Class Participation  40% : Midterm Exam One-hour close-book Exam (7/27, Week 7) Take-home Exam (Due 8/17, Week 10)  50% : Final Project 10% : Proposal (problem analysis) 10% : Final Report 30% : Data Analysis and Presentation

9. 9 Project Proposal (8/24, Week 11) The proposal is to plan your project. It should at least include :  Title  Team member  Motivation  Problem and data description including data source, description, description of important attributes, data year, record number, attribute number and other  Schedule  A short description of the used DM techniques  Data analysis process data preprocessing, data mining, knowledge presentation/evaluation  Performance evaluation method  Others

10. 10 Final Project  A project on DM application  Use Weka to analyze a certain data set  A presentation and report to introduce your project, at least including Motivation Problem, data description, data range, basic data statistics How the problem can be solved The DM algorithms you use/implement and related literature Analysis process data preprocessing, data mining, knowledge presentation/evaluation Performance evaluation method Result and value of the discovered knowledge Discussion  Each student can use 22 min for presentation 15~17 min for presentation, 3 min for Q&A, 2 min for getting ready

11. 11 Class Schedule  Week 1:Class Introduction / Introduction to data mining  Week 2-3:Association rule mining  Week 4-5: Classification and prediction  Week 6-7:Cluster analysis (One-hour Close-book Exam @ Week 7)  Week 8:The applications of data mining ( 林詩偉老師 )  Week 9:Mass data analysis ( 林詩偉老師 )  Week 10: Introduction of Weka (free DM software) (Take-home Exam due 8/17)  Week 11: Data preprocessing (proposal of final project due at Week 11)  Week 12:Data warehouse  Week 13: Final project presentation

12. 12 Class Schedule  Week 1:Class Introduction / Introduction to data mining  Week 2-3:Classification and prediction, W3 6/22: 6:40pm  Week 4-5: Cluster analysis, W5 7/6: 6:40pm  Week 6-7:Association rule mining, W7 7/27: 6:40pm (One-hour Close-book Exam @ Week 7)  Week 8:The applications of data mining ( 林詩偉老師 )  Week 9:Mass data analysis ( 林詩偉老師 ) (Take-home Exam due 8/10)  Week 10: 8/17 停課 ( 課程分攤於 W3, W5, W7, W12 進行 )  Week 11: Data preprocessing (proposal of final project due 8/24 at Week 11)  Week 12:Doing Project by Weka, W12 8/31: 6:40pm  Week 13: Final project presentation W3, W5, W7 : Introduction of Weka (free DM software)

13. 13 Internet Resources  Lecture Slides Browser URL: ftp://163.25.117.117/cchen 102Summer →102S_Data Mining_eMIS → 上課投影片  Open source DM software in Java: Weka 3.x.x http://www.cs.waikato.ac.nz/~ml/weka/index.html

14. 14 Dataset Web Sites for Mining  UCI Machine Learning Repository http://www1.ics.uci.edu/~mlearn/MLRepository.html  DASL http://lib.stat.cmu.edu/DASL/Datafiles/  JSE Data Archive http://www.amstat.org/publications/jse/jse_data_archive.html  KDNuggets http://www.kdnuggets.com/datasets/index.html  MLnet Online Information Service http://www.mlnet.org/cgi-bin/mlnetois.pl/?File=datasets.html

15. 15 Question & Answer