Download KDD and Data Mining Syllabus for 2008

Fu Jen Catholic University
2008 Spring
Department / Code
(開課單位/單位代碼)
G7461
Course Code
(課程代碼)
09637
Course Name
知識探索與資料採擷
(課程名稱)
Knowledge Discovery and Data Mining
Credit
F
(學分數)
S
3
This course teaches students concepts of knowledge discovery and data mining. By
introducing various data mining algorithms, the course teaches students to understand how to
Course Objectives
(課程目標)
analyze large volume of data in order to find knowledge and interesting patterns. Moreover,
this course investigates business cases to let students learn how to implement useful data
mining tasks in the real world.
Prerequisites
(先修課程)
Course Materials
(課程教材)
1. “Introduction to Data Mining”, 2005, Author: Pang-Ning Tan, Michael
Steinbach, Vipin Kumar, Publisher: Addison Wesley.
2. “Data Mining, Concepts and Techniques”, 2006, 2nd Edition, Jiawei Han &
Michael Kamber, Publisher: Morgan Kaufmann.
1.
“Data Mining Techniques -- For Marketing, Sales, and Customer Support”, 2004, 2nd
edition, Author: Michael J. Berry, Gordon S. Linoff., Publisher: Wiley Computer
Reference
Publishing.
(參考書目)
2.
“Data Mining with SQL Server 2005”, 2005, Author: ZhaoHui Tang and Jamie
MacLennan, Publisher: Wiley Publishing, Inc.
課堂之前測(Pre-test)
課堂中的隨堂測試(Quiz)
% 期末報告/論文撰述(Team Paper/Theses Writing)
10 %
期中考(筆試)(Midterm Test)
Evaluation
(評量方式)
%
Pedagogical Methods
(教學方法)
█心得/作業撰寫(Assignment)
10 %
課堂上實作演練(Role Playing)
%
% 專業團體之證照檢定(Certification)
%
學生表現側寫報告(Profile Report)
%
個別面試或口試(Oral Exam)
20 %
%
40 %
20
█課堂參與(Class Participation)
專題發表(Presentation)
█課堂後測/期末考(筆試)(Final Test)
█個案分析報告撰寫(Case Report)
%
%
其他(Others)
█講授(Lecture)
競賽讀書會(Study Group)
█個案教學(Case Study)
專題實作(Seminar on Field Research)
電子教學(e-Learning)
產業實習(Internship)
體驗教學(Project Adventure)
服務學習實作(Service Learning)
角色扮演實境教學(Role Playing)
自主學習(Independent Study)
企業競賽遊戲(Business Simulation Game)
對話教學法(Dialogue Teaching)
管理電影(Theater Learning)
其他
%
Course Web
http://www.elearn.fju.edu.tw/icanxp/iCANPortal/index.html (iCAN 教學網)
(課程網頁)
Week
Course Outline
Date
Topic
1
2/25
Introduction: What is Data Mining?
2
3/03
Approaches to Data Mining.
3
3/10
Data Mining Techniques: Association Rules Mining.
4
3/17
Data Mining Techniques: Association Rules Mining.
5
3/24
Data Mining Techniques: Classification and Prediction.
6
4/31
Data Mining Techniques: Classification and Prediction.
7
4/07
Off (Spring Break)
8
4/14
Data Mining Techniques: Clustering Analysis.
9
4/21
Midterm Exam
期中
(課程大綱進度)
考週
10
4/28
Data Mining Techniques: Clustering Analysis.
11
5/05
Data Preparation and preprocessing.
12
5/12
Data Warehouse and OLAP Technology for Data Mining.
13
5/19
Mining Complex Types of Data.
14
5/26
Mining Complex Types of Data.
15
6/02
Applications and Trends in Data Mining.
16
6/09
Final Exam
期末
考週
Contribution to
全人教育
█做中學
Mission
(Holistic Education)
(Learning by doing)
(本課程與管理學院
人本價值
█整合資源
█創新知識
國際視野
使命之關係)
(Human-centric values)
(Resource integration)
(Innovative knowledge)
(International view)
Name: 翁頌舜 (Sung-Shun Weng), Ph.D.
Instructor
(老師資料)
E-mail: [email protected]
Phone: (02) 2905-2717
Office Hour: Thursday: 4:40PM—6:30PM.
Room: BS112-3