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INTRODUCTION TO DATA MINING (數據挖掘)

1) For undergraduate students in Computer Science major

2) South campus, GDUFS

3) Starting at September 2016; March 2017; September 2017; March 2018

4) 48 study scores in total

5) Scheduled as 16 weeks in total (16*3, 1~2 weeks for each chapter)

Teaching Materials:

1) 《數據挖掘原理與實踐》，蔣盛益等著，電子工業出版社，2011

2) 《商務數據挖掘與應用案例分析》，蔣盛益著，電子工業出版社，2014

3) Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining.

(美)譚,(美)斯坦巴赫 著,范明等譯.數據挖掘導論, 圖靈計算機科學叢書. 人民郵電出版社.2006

4) Jiawei Han, Micheline Kamber, Jian Pei. Data Mining: Concepts and Techniques
(加)韓家煒,堪博(Kam ber, M.)著,范明,孟小峰譯.數據挖掘概念與技術(原書第2版). 機械工業出版社.2007

5) Ian H. Witten, Eibe Frank, Mark A. Hall. Data Mining Practical Machine Learning Tools and Techniques.Morgan Kaufmann Publishers
Ian H. Witten, Eibe Frank, Mark A. Hall 著, 李川等譯. 數據挖掘-實用機器學習工具與技術. 機械工業出版社

6) 袁梅宇著. 數據挖掘與機器學習WEKA應用技術與實踐. 清華大學出版社. 2014

7) Peter Harrington. Machine Learning in Action.
(美)Peter Harrington, 李銳等譯. 機器學習實戰.圖靈計算機科學叢書. 人民郵電出版社. 2013

8) Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman. Mining of Massive Datasets (Second Edition)
(美)Jure Leskovec等著, 王斌譯. 互聯網大規模數據挖掘與分布式處理.圖靈計算機科學叢書. 人民郵電出版社. 2015

Practice resource:

1) Weka 3: Data Mining Software in Java

2) Downloading and installing Weka

3) General Weka documentation (manual Weka 3.8.0)

4) Collections of Datasets

Related resources:

1) 《Introduction to Data Mining》 by Pang-Ning Tan, Michael Steinbach, Vipin Kumar, University of Minnesota, course resource

2) 《Introduction to Data Mining》 by Prof. Qiang Yang, 香港科技大學, course resource

3) 《Data Mining》 by Prof. Chris Clifton, Purdue University, course resource