| 當期課號 |
7433 |
Course Number |
7433 |
| 授課教師 |
李金鳳 |
Instructor |
LEE,CHIN FENG |
| 中文課名 |
資料探勘 |
Course Name |
Data Mining |
| 開課單位 |
資訊科技研究所博士班一A |
Department |
|
| 修習別 |
選修 |
Required/Elective |
Elective |
| 學分數 |
3 |
Credits |
3 |
| 課程目標 |
資料探勘是一個多學科領域，從多個學科汲取營養。這些學科包括資料庫技術、人工智慧、機器學習、神經網絡、統計學、模式識別、知識庫系統、知識獲取、資訊檢索、高性能計算和資料視覺化。資料探勘是對資料的處理、分析及建立模式，以找出有意義的隱藏資訊。本課程的目的不在介紹資料探勘所發展的計算分析方法，也學習資料倉儲的相關技術。主要的內容包含資料倉儲的意義與建置方式，資料概念描述：特徵化與比較、分類、關聯技術、群集分析。 |
Objectives |
Data Mining and Knowledge Discovery has become an active area of research, attracting people from several disciplines, including database systems, statistics, information retrieval, pattern recognition, AI/machine learning, and data visualization.
The course will introduce data mining and data warehousing, and study their principles, algorithms, implementations, and applications.
TOPICS:
An introduction to data mining and data warehousing: motivation and applications. Basic data warehousing technology: data cube methods, data warehouse construction and maintenance.
Basic data mining techniques: characterization, association, classification, clustering, and similarity-based mining.
Advanced data mining applications: mining relational and transaction data, mining time-related data, spatial data mining, textual data mining, multimedia data mining, visual data mining, and Web mining. |
| 教材 |
Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber , Morgan Kaufmann Pub., 2000.
Data Mining: Introductory and Advanced Topics, Dunham, Prentice Hall, 2002.
Selected Journal or Conference Papers
REFERENCES:
Some recent conference/journal paper collection, (class distribution). |
Teaching Materials |
|
| 成績評量方式 |
作業+報告+平時表現(出席率+上課Q&A)+其他、期中與期末考試、期末計畫書+專題報告+期末專題文件)。 |
Grading |
Presentation+numbers of question+Assignments + Class presentation Midterm &Final termsProject and project documentation。 |
| 教師網頁 |
|
| 教學內容 |
TOPICS:
An introduction to data mining and data warehousing: motivation and applications.
Basic data warehousing technology: data cube methods, data warehouse construction and maintenance.
Basic data mining techniques: characterization, association, classification, clustering, and similarity-based mining.
Advanced data mining applications: mining relational and transaction data, mining time-related data, spatial data mining, textual data mining, multimedia data mining, visual data mining, and Web mining. |
Syllabus |
OBJECTIVE/DESCRIPTION:
Data Mining and Knowledge Discovery has become an active area of research, attracting people from several disciplines, including database systems, statistics, information retrieval, pattern recognition, AI/machine learning, and data visualization.
The course will introduce data mining and data warehousing, and study their principles, algorithms, implementations, and applications. |