Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2018 241639 Data Mining 1 3.0 3 ★ HUANG YO-PING GEE
IMEECS 31 4 English taught only.

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2018	241639	Data Mining	1	3.0	3	★	HUANG YO-PING	GEE IMEECS	31	4	English taught only.

Course Plan：

Instructor HUANG YO-PING

Email yphuang@ntut.edu.tw

Last Updated 2017-12-14 10:12:00

Course Objective Scope: This course is to introduce useful data mining techniques for students. We hope that students can learn more fundamental and advanced skills in the competitive mobile commerce world. 資料探勘是從大量資料中尋找有用的資訊，是一個跨越統計、資料庫和人工智慧的學門。本課程著重在從資料庫的觀點來看資料探勘的意義，並深入探討幾種資料探勘功能及其技術，最後並以實例討論來驗證資料探勘的應用價值。探討議題包括資料探勘概念、資料探勘功能、資料探勘步驟、資料倉儲、決策樹分析集群分析、關聯分析與購物籃分析等。

Course Schedule 1: Introduction 2~3: Data preprocessing 4~6: Mining association rules in large databases 7~8: Classification and prediction 9: Mid-term exam 10~11: Cluster analysis 12~13: Mining complex types of data 14~15: Spatial and temporal data mining 16~17: Advanced topics in data mining 18: Final exam

Evaluation and grading policy 1. Mid-term 30% 2. Final-exam 30% 3. Quizzes+HWs 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Textbook: J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers Reference: M.H. Dunham, Data mining: introductory and advanced topics, 歐亞書局代理

The access to curricular consultation

Note
　

Instructor	HUANG YO-PING
Email	yphuang@ntut.edu.tw
Last Updated	2017-12-14 10:12:00
Course Objective	Scope: This course is to introduce useful data mining techniques for students. We hope that students can learn more fundamental and advanced skills in the competitive mobile commerce world. 資料探勘是從大量資料中尋找有用的資訊，是一個跨越統計、資料庫和人工智慧的學門。本課程著重在從資料庫的觀點來看資料探勘的意義，並深入探討幾種資料探勘功能及其技術，最後並以實例討論來驗證資料探勘的應用價值。探討議題包括資料探勘概念、資料探勘功能、資料探勘步驟、資料倉儲、決策樹分析集群分析、關聯分析與購物籃分析等。
Course Schedule	1: Introduction 2~3: Data preprocessing 4~6: Mining association rules in large databases 7~8: Classification and prediction 9: Mid-term exam 10~11: Cluster analysis 12~13: Mining complex types of data 14~15: Spatial and temporal data mining 16~17: Advanced topics in data mining 18: Final exam
Evaluation and grading policy	1. Mid-term 30% 2. Final-exam 30% 3. Quizzes+HWs 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Textbook: J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers Reference: M.H. Dunham, Data mining: introductory and advanced topics, 歐亞書局代理
The access to curricular consultation
Note