1 IMM472 資料探勘 陳春賢

2 Lecture I Class Introduction

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 Contact Info  Office Hour: Friday 3:00 – 5:00 pm  Contact Info: TEL: (03) ext

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 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 Textbook and References  Textbook Jiawei Han and Micheline Kamber, Data Mining : Concepts and Techniques, 2nd edition, Morgan Kaufmann Publishers, San Francisco, CA, USA,  參考書 Margaret H. Dunham, Data Mining: Introductory and Advanced Topics, Prentice Hall, Upper Saddle River, NJ, USA, 2002.

8 Grading Policy  10% : Class Participation  40% : Midterm Exam  50% : Final Project 10% : Proposal (problem analysis) 10% : Final Report 30% : Data Analysis and Presentation

9 Project Proposal (week 14, 5/31) The proposal is to plan your project. It should at least include :  Title  Team member  Motivation  Problem, data description, and importance of data including data source, description, description of important attributes, data year, record number, attribute number and other  Schedule and who does what if your team has two members  Used data mining techniques  A short description of the DM techniques  The process flow of data analysis  Expected value of the discovered knowledge  Others

10 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 Result and value of the discovered knowledge Discussion

11 Class Schedule  Week 1:Introduction of class and data mining  Week 2-4: Association rule mining  Week 5-7: Classification and prediction  (Week 6 : 民族掃墓節 放假一日 )  Week 8-10:Cluster analysis  Week 11: Midterm  Week 12-14: Data preprocessing, DW/OLTP (Week 14 : project proposal due)  Week 15: Data warehouse and OLTP  Week 16-18: Final project presentation

12 Internet Resources  Lecture Slides Browser URL: ftp:// / cchen → 102Spring → 102S_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

13 Dataset Web Sites for Mining  UCI Machine Learning Repository  Google Trends 、 Google Insights for Search Google Trends Google Insights for Search  DASL  JSE Data Archive  KDNuggets  MLnet Online Information Service

14 Question & Answer