系所

資訊科技與管理研究所

1 年級

課號 / 班別

ms1211 / B

3 學分

科目中文名稱

資料倉儲與探勘

科目英文名稱

Data Warehouse and Data Mining

每週授課時數

3 小時

專業選修

任課 老師

廖文華

開課期間

一學期 

人數上限

15 人

一、教學目標

Data warehouse and data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, statistics, pattern recognition, knowledge-based systems, etc. This course offers a comprehensive, practical look at the related concepts and techniques.

二、先修科目

No.

三、教材內容

1. Introduction

1.1 What Motivated Data Mining? Why Is It Important?

1.2 So, What Is Data Mining?

1.3 Data Mining--On What Kind of Data?

1.4 Data Mining Functionalities—What Kinds of Patterns Can Be Mined?

2.Data Preprocessing

2.1 Why Preprocess the Data?

2.2 Descriptive Data Summarization

2.3 Data Cleaning

2.4 Data Integration and Transformation

2.5 Data Reduction

2.6 Data Discretization and Concept Hierarchy Generation

3.Data Warehouse and OLAP Technology

3.1 What Is a Data Warehouse?

3.2 A Multidimensional Data Model

3.3 Data Warehouse Architecture

3.4 Data Warehouse Implementation

3.5 From Data Warehousing to Data Mining

4.Data Cube Computation and Data Generalization

4.1 Efficient Methods for Data Cube Computation

4.2 Further Development of Data Cube and OLAP Technology

4.3 Attribute-Oriented Induction—An Alternative Method for Data Generalization and Concept De- scription

5.Mining Frequent Patterns, Associations, and Correlation

5.1 Basic Concepts and a Road Map

5.2 Efficient and Scalable Frequent Itemset Mining Methods

5.3 Mining Various Kinds of Association Rules

5.4 From Association Mining to Correlation Analysis

5.5 Constraint-Based Association Mining

6.Classification and Prediction

6.1 What Is Classification? What Is Prediction?

6.2 Issues Regarding Classification and Prediction

6.3~6.9 Classification Methods

7.Cluster Analysis

7.1 What Is Cluster Analysis?

7.2 Types of Data in Cluster Analysis

7.3 A Categorization of Major Clustering Methods

7.4~7.8 Clustering Methods

四、教學方式 

1. 教師授課

2. 同學上台 paper 報告

五、參考書籍 

Jiawei Han and Micheline Kamber

Data Mining Concepts and Techniques 2/e

Morgan Kaufmann, 2007.

六、教學進度 

1-2 週 Introduction 

3-4 週 Data Preprocessing

5-6 週 Data Warehouse and OLAP Technology

7-8 週 Data Cube Computation and Data Generalization

9 週 期中考

10-11 週 Mining Frequent Patterns, Associations, and Correlation

12-14 週 Classification and Prediction

15-17 週 Cluster Analysis

18 週 期末考

七、評量方式 

1. 期中考 30%

2. 期末考 30%

3. paper 報告 30%

4. 上課出席 10%

八、講義位址