Syllabus
教學大綱
Department of Industrial and
Information Management
Course(課程): Data
Mining (Spring, 2015); W B-D
Textbook: Data Mining – Witten
and Frank, and Hall, 3rd Edition 2011, Morgan Kaufmann.
Instructor: Wong, Tzu-Tsung (翁慈宗), office: 61303, ext: 53722, tzutsung@mail.ncku.edu.tw
Content: Data mining is a
technique for retrieving information from data.
The information will be useful in achieving some specific purpose. This course introduces data mining tools for
classification, association, clustering, and numeric prediction. Students will be asked to use Weka to
accomplish a data mining project. The
topics that will be covered in this course are
(內容：數據挖掘是從數據中提取信息的技術。該信息將在實現某些特定的用途是有用的。本課程介紹了分類，關聯，聚類和數值預測的數據挖掘工具。學生將被要求使用Weka中完成數據挖掘項目。這將包括在本課程的主題)
· Introduction to data ming (介紹數據挖掘)
· Preparation of input data (輸入數據的準備)
· Data transformation (數據轉換)
· Classification methods (分類方法)
· Association analysis (關聯分析)
· Numeric Prediction methods (數值預測方法)
· Clustering methods (聚類方法)
.
Grade: Homework
assignments 40%, midterm exam 15%, term paper report 20%, Final exam 25%
(評分：家庭作業40％，期中考試15％，期末論文報告20％，期末考試25％)
Homework: There is
approximately one assignment for every month. In general, the time for
completing an assignment is a week. Copying assignments in any way is strictly
prohibited. No late homework except for acceptable excuses.
(家庭作業：對於每月大約分配。在一般情況下，該時間用於完成工作分配是一個星期。以任何方式抄襲作業被嚴格禁止的。任何遲交的作業，除了可以接受的藉口)
Exam schedule：Midterm
4/29, Final 6/24
(考試日程安排：期中4/29，期末6/24)
DATA
MINING產品特點:
解釋了數據挖掘算法是如何工作的。
幫助選擇合適的方法來特別的問題，並以比較和評價的不同技術的結果。
包括性能改進技術，包括輸入預處理並結合輸出不同的方法。
向您介紹如何使用的Weka機器學習工作台。
Explains
how data mining algorithms work.
Helps you
select appropriate approaches to particular problems and to compare and
evaluate the results of different techniques.
Covers
performance improvement techniques,
including input preprocessing and combining output from different methods.
Shows you
how to use the Weka machine learning workbench.