資料探勘專題
Special Topics on Data Mining
| 節 | 週二 |
|---|---|
A 18:30–19:20 | 資料探勘專題 MB304（光復） 3 節連堂 |
B 19:30–20:20 | |
C 20:30–21:20 |
* 根據陽明交大上課時間表所列
課程概述與目標:The main objective of this class is to explore research topics on techniques and applications of data mining, drawing work from areas including database technology, artificial intelligence, machine learning and knowledge-based systems. Selected research papers from conference proceedings and journals will be discussed. The course will cover research topics relating to mining association rules, mining sequence data, classification, clustering, process mining and text mining. Moreover, the course emphasizes on the practices of applying data mining techniques to various applications, including recommender systems, knowledge support systems, social network analysis, patent analysis and problem solving. Core research skills of literature analysis, innovation, evaluation of new ideas, and communication are emphasized via paper presentation and discussion.
Advanced database management systems
無備註
Paper Survey or Project (40%), Presentation & Discussion (40%), Others (20%)
Paper Survey or Project (40%), Presentation & Discussion (40%), Others (20%)
Overview; Recommender systems
1. Content-based filtering 2. Collaborative filtering 3. Hybrid; Association 4. Document Recommendation
- 講授：
- 12
Text Mining & Knowledge Engineering
1. Text mining 2. Information retrieval & filtering 3. Knowledge support 4. Task relevant knowledge
- 講授：
- 9
Social Network Analysis & Link Analysis
1. Social network analysis 2. Link analysis 3. Community of Practice 4. Recommendations
- 講授：
- 6
Knowledge Flow & Process Mining
1. Knowledge flow 2. Process mining 3. Knowledge flow mining 4. Knowledge support & sharing
- 講授：
- 9
Review Mining
1. Review analysis 2. Review mining and Recommendation
- 講授：
- 6
Problem Solving & Case-based Reasoning
1. Problem solving 2. Case-based reasoning 3. Knowledge support
- 講授：
- 6
| 週次 | 主題 |
|---|---|
| 第 1 週 | Overview |
| 第 2 週 | Content-based filtering; Collaborative filtering |
| 第 3 週 | Hybrid; Association-based recommendation |
| 第 4 週 | Document recommendation |
| 第 5 週 | Text mining |
| 第 6 週 | Information retrieval & filtering |
| 第 7 週 | Knowledge support; Task relevant knowledge |
| 第 8 週 | Social network analysis; Link analysis |
| 第 9 週 | Community of Practice; Recommendations |
| 第 10 週 | Paper Survey (Project): Mid-Presentation & Report |
| 第 11 週 | Knowledge flow |
| 第 12 週 | Process mining; Knowledge flow mining |
| 第 13 週 | Knowledge support & sharing |
| 第 14 週 | Review analysis |
| 第 15 週 | Review mining and Recommendation |
| 第 16 週 | Problem solving; Case-based reasoning |
| 第 17 週 | Knowledge support for Problem Solving |
| 第 18 週 | Paper survey (Project) : Final Presentation & Report |
Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann Publishers, 2006, by Jiawei Han and Micheline Kamber Data Mining: Concepts, Models, Methods, and Algorithms, IEEE press, 2003, by Mehmed Kantardzic Paper Readings
- 地點
- MB305
- 時間
- Tuesday Pm 5:30 ~ 6:30
- 聯絡方式
- dliu@iim.nctu.edu.tw