Download 資料探勘及應用評分標準(Grades) 參考書/教科書(Textbooks) 課程

資料探勘及應用
98 學年度 第 2 學期
2/2010
Data Mining with Application
http://ke.cse.nsysu.edu.tw/~chlin/dm.html
林葭華 (Cha-Hwa Lin), F9035, x5899
[email protected]
Office hours: 週二 (Tue) 14-16, 週三(Wed) 12:10-14
課程大綱 (Course Description)
This course provides an introduction to the data mining process by explaining how it can be used to solve
real problems. The most aspects of data mining and knowledge discovery, focusing on model building
and testing as well as on interpreting and validation results are discussed. The purpose of this course is to
introduce the students to various data mining concepts and algorithms.
授課方式 (Course Activities)
1. Lecture 2. Discussion 3. Case study
評分標準 (Grades)
1. Midterm exam (25%) 2. Final exam (25%) 3. Homework (40%) 4. Participation (10%)
參考書/教科書 (Textbooks)
主要教科書 (Required):
Margaret H. Dunham, 2002. Data Mining: Introductory and Advanced Topics, Prentice Hall, New Jersey,
USA.
參考書目 (Other Good DM Books):
1. Tom M. Mitchell, 1997. Machine Learning, McGraw-Hill, New York, USA.
2. Pang-Ning Tan, M. Steinbach, and V. Kumar, 2005. Introduction to Data Mining, Addison Wesley,
New York, USA.
3. Richard O. Duda, Peter E. Hart, and David G. Stork, 2001. Pattern Classification, 2nd Edition, Wiley,
New York, USA.
4. T. Hastie, R. Tibshirani, and J. H. Friedman, 2001/2003. The Elements of Statistical Learning,
Springer, New York, USA.
課程內容、進度、與閱讀文獻 (Course Topics)
I. INTRODUCTION.
1. Introduction.
2. Related Concepts.
3. Data Mining Techniques.
II. CORE TOPICS.
4. Classification.
5. Clustering.
6. Association Rules.
III. ADVANCED TOPICS.
7. Web Mining.
8. Spatial Mining.
9. Temporal Mining.
All course requirements are subject to change.
Changes will be announced during class sessions.