1. 1 IMM472 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  Office Hour: Friday 3:00 – 5:00 pm  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 certain data sets

6. 6 Course Content  Introduction to data mining  Main data mining techniques Association rule mining Classification and prediction Cluster analysis  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.

8. 8 Grading Policy  10% : Class Participation  40% : Midterm Exam  50% : Final Project 5% : Proposal (problem analysis) 10% : Final Report 35% : Data Analysis and Presentation

9. 9 Project Proposal (week 13, 5/22) The proposal is to plan your project. It should at least include :  Title  Student Name and Number  Motivation  Problem, data description, and importance of data including data source, description, description of important attributes, data year, record number, attribute number and other  Project schedule  Used data mining techniques  A short description of the DM techniques  The process flow of data analysis  Performance evaluation method  Expected value of the discovered knowledge  Others

10. 10 Report and Presentation of Final Project  A project on DM application  A presentation and report to introduce your project, at least including Motivation Problem, data description, and importance of data How the problem can be solved The DM algorithms you use/implement and related literature The process flow of data analysis data preprocessing, data mining, knowledge presentation/evaluation Class distribution at each attribute Performance evaluation method Result and value of the discovered knowledge Discussion

11. 11 Class Schedule  Week 1:Introduction of class and data mining  Week 2-4: Association rule mining  Week 5-7: Classification and prediction  Week 8-10:Cluster analysis  Week 11: Midterm  Week 12-13: Data preprocessing (Week 13 : project proposal due)  Week 14-15: Data warehouse and OLTP  Week 16-18: Final project presentation (5 presentations each week)

12. 12 Internet Resources  Lecture Slides Browser URL: ftp://163.25.117.117/ cchen → 103Spring → 103S_Data Mining 上課計畫、上課投影片、期末專題、 Weka 、老師學期週行程  Open source DM software in Java: WEKA http://www.cs.waikato.ac.nz/~ml/weka/index.html Weka 使用簡介.doc Attribute-Relation File Format (ARFF).htm Data (ARFF, CSV formats)

13. 13 Dataset Web Sites for Mining  UCI Machine Learning Repository http://www1.ics.uci.edu/~mlearn/MLRepository.html  衛生福利部食品藥物管理署 OPEN DATA 開放資料集集 http://data.fda.gov.tw  Google Trends 、 Google Insights for Search Google Trends Google Insights for Search  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

14. 14 Question & Answer