Data Mining 資料探勘 Tamkang University Introduction to Data Mining ( 資料探勘導論 ) 1022DM01 MI4 Wed, 6,7 (13:10-15:00) (B216) Min-Yuh Day 戴敏育 Assistant Profes

Size: px

Start display at page:

Download "Data Mining 資料探勘 Tamkang University Introduction to Data Mining ( 資料探勘導論 ) 1022DM01 MI4 Wed, 6,7 (13:10-15:00) (B216) Min-Yuh Day 戴敏育 Assistant Profes"

派茸陶
5 years ago
Views:

1 Data Mining 資料探勘 Tamkang University Introduction to Data Mining ( 資料探勘導論 ) 1022DM01 MI4 Wed, 6,7 (13:10-15:00) (B216) Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept. of Information Management, Tamkang University 淡江大學資訊管理學系 tku.edu.tw/myday/

2 淡江大學 102 學年度第 2 學期課程教學計畫表 Spring 2014 ( ) 課程名稱 : 資料探勘 (Data Mining) 授課教師 : 戴敏育 (Min- Yuh Day) 開課系級 : 資管四 P (TLMXB4P) 開課資料 : 選修單學期 2 學分 (2 Credits, ElecHve) 上課時間 : 週三 6,7 (Wed 13:10-15:00) 上課教室 :B216 2

3 課程簡介本課程介紹資料探勘 (Data Mining) 的基礎概念及應用技術課程內容包括資料探勘導論關連分析分類與預測分群分析文字探勘與網頁探勘海量資料分析 SAS 企業資料採礦實務與認證 (SAS EM) 資料探勘個案分析與實作 3

4 Course IntroducDon This course introduces the fundamental concepts and applicahons technology of data mining. Topics include IntroducHon to Data Mining AssociaHon Analysis ClassificaHon and PredicHon Cluster Analysis Text and Web Mining Big Data AnalyHcs Data Mining Using SAS Enterprise Miner (SAS EM) Case Study and ImplementaHon of Data Mining 4

5 課程目標 (ObjecDve) 瞭解及應用資料探勘基本概念與技術 Understand and apply the fundamental concepts and technology of data mining 5

6 課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) 1 103/02/19 資料探勘導論 (IntroducHon to Data Mining) 2 103/02/26 關連分析 (AssociaHon Analysis) 3 103/03/05 分類與預測 (ClassificaHon and PredicHon) 4 103/03/12 分群分析 (Cluster Analysis) 5 103/03/19 個案分析與實作一 (SAS EM 分群分析 ): Case Study 1 (Cluster Analysis K- Means using SAS EM) 6 103/03/26 個案分析與實作二 (SAS EM 關連分析 ): Case Study 2 (AssociaHon Analysis using SAS EM) 7 103/04/02 教學行政觀摩日 (Off- campus study) 8 103/04/09 個案分析與實作三 (SAS EM 決策樹模型評估 ): Case Study 3 (Decision Tree, Model EvaluaHon using SAS EM) 6

7 課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) 9 103/04/16 期中報告 (Midterm Project PresentaHon) /04/23 期中考試週 (Midterm Exam) /04/30 個案分析與實作四 (SAS EM 迴歸分析類神經網路 ): Case Study 4 (Regression Analysis, ArHficial Neural Network using SAS EM) /05/07 文字探勘與網頁探勘 (Text and Web Mining) /05/14 海量資料分析 (Big Data AnalyHcs) /05/21 期末報告 (Final Project PresentaHon) /05/28 畢業考試週 (Final Exam) 7

8 教學方法與評量方法教學方法講述討論實作評量方法實作報告上課表現 8

9 講義 (Slides) 參考書籍教材課本 Applied AnalyHcs Using SAS Enterprise Mining, Jim Georges, Jeff Thompson and Chip Wells, 2010, SAS Decision Support and Business Intelligence Systems, Ninth EdiHon, Efraim Turban, Ramesh Sharda, Dursun Delen, 2011, Pearson 決策支援與企業智慧系統, 九版,Efraim Turban 等著, 李昇暾審定,2011, 華泰 9

10 作業與學期成績計算方式作業篇數 3 篇學期成績計算方式 R 期中評量 :30 % R 期末評量 :30 % R 其他 ( 課堂參與及報告討論表現 ): 40 % 10

11 Team Term Project Term Project Topics Data mining Web mining Business Intelligence 3-4 人為一組分組名單於 2014/03/05 ( 三 ) 課程下課時繳交由班代統一收集協調分組名單 11

12 Data Mining at the IntersecDon of Many Disciplines Artificial Intelligence Statistics Pattern Recognition DATA MINING Machine Learning Mathematical Modeling Databases Management Science & Information Systems Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 12

13 Knowledge Discovery (KDD) Process Data mining: core of knowledge discovery process Pattern Evaluation Data Mining Task-relevant Data Data Warehouse Selection Data Cleaning Data Integration Databases Source: Han & Kamber (2006) 13

14 Data Warehouse Data Mining and Business Intelligence Increasing potential to support business decisions Decision Making Data PresentaDon Visualization Techniques Data Mining Information Discovery End User Business Analyst Data Analyst Data ExploraDon Statistical Summary, Querying, and Reporting Data Preprocessing/IntegraDon, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems DBA Source: Han & Kamber (2006) 14

15 Business Pressures Responses Support Model Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 15

16 A Taxonomy for Data Mining Tasks Data Mining Learning Method Popular Algorithms Prediction Supervised Classification and Regression Trees, ANN, SVM, Genetic Algorithms Classification Supervised Decision trees, ANN/MLP, SVM, Rough sets, Genetic Algorithms Regression Supervised Linear/Nonlinear Regression, Regression trees, ANN/MLP, SVM Association Unsupervised Apriory, OneR, ZeroR, Eclat Link analysis Unsupervised Expectation Maximization, Apriory Algorithm, Graph-based Matching Sequence analysis Unsupervised Apriory Algorithm, FP-Growth technique Clustering Unsupervised K-means, ANN/SOM Outlier analysis Unsupervised K-means, Expectation Maximization (EM) Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 16

17 The EvoluDon of BI CapabiliDes Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 17

18 A High- Level Architecture of BI Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 18

$Mining the Social Web: Analyzing Data from Facebook, Twi\er, LinkedIn, and Other Social$

19 Mining the Social Web: Analyzing Data from Facebook, Twi\er, LinkedIn, and Other Social Media Sites Source: 19

Interactions Web Analytics Voice of Customer Customer Experience Management Knowledge

20 Web Mining Success Stories Amazon.com, Ask.com, ScholasHc.com, Website OpHmizaHon Ecosystem Customer Interaction on the Web Analysis of Interactions Web Analytics Voice of Customer Customer Experience Management Knowledge about the Holistic View of the Customer Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 20

21 Business Intelligence Trends 1. Agile InformaHon Management (IM) 2. Cloud Business Intelligence (BI) 3. Mobile Business Intelligence (BI) 4. AnalyHcs 5. Big Data Source: 21

22 Business Intelligence Trends: CompuDng and Service Cloud CompuHng and Service Mobile CompuHng and Service Social CompuHng and Service 22

23 Business Intelligence and AnalyDcs Business Intelligence 2.0 (BI 2.0) Web Intelligence Web AnalyHcs Web 2.0 Social Networking and Microblogging sites Data Trends Big Data Plaiorm Technology Trends Cloud compuhng plaiorm Source: Lim, E. P., Chen, H., & Chen, G. (2013). Business Intelligence and Analytics: Research Directions. ACM Transactions on Management Information Systems (TMIS), 3(4), 17 23

24 Business Intelligence and AnalyDcs: Research DirecDons 1. Big Data AnalyHcs Data analyhcs using Hadoop / MapReduce framework 2. Text AnalyHcs From InformaHon ExtracHon to QuesHon Answering From SenHment Analysis to Opinion Mining 3. Network Analysis Link mining Community DetecHon Social RecommendaHon Source: Lim, E. P., Chen, H., & Chen, G. (2013). Business Intelligence and Analytics: Research Directions. ACM Transactions on Management Information Systems (TMIS), 3(4), 17 24

25 Source: Davenport, T. H., & Patil, D. J. (2012). Data Scientist. Harvard business review 25

26 Big Data

27 Summary This course introduces the fundamental concepts and applicahons technology of data mining. Topics include IntroducHon to Data Mining AssociaHon Analysis ClassificaHon and PredicHon Cluster Analysis Text and Web Mining Big Data AnalyHcs Data Mining Using SAS Enterprise Miner (SAS EM) Case Study and ImplementaHon of Data Mining 27

28 Contact InformaDon 戴敏育博士 (Min-Yuh Day, Ph.D.) 專任助理教授淡江大學資訊管理學系電話 : #2347 傳真 : 研究室 :i716 ( 覺生綜合大樓 ) 地址 : 新北市淡水區英專路 151 號 myday@mail.tku.edu.tw 網址 : 28

商業智慧實務 Prac&ces of Business Intelligence Tamkang University 商業智慧導論 (Introduction to Business Intelligence) 1022BI01 MI4 Wed, 9,10 (16:10-18:00) (B113)

商業智慧實務 Prac&ces of Business Intelligence Tamkang University 商業智慧導論 (Introduction to Business Intelligence) 1022BI01 MI4 Wed, 9,10 (16:10-18:00) (B113) Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept. of