本課程從統計和視覺化分析角度給予大數據資料更多面向的思維,連結至集群分析、決策樹、類神經網路及迴歸/羅吉斯迴歸預測模型等資料採礦方法，藉由SAS Visual Analytics, SAS Enterprise Guide (SAS EG) 和 SAS Enterprise Miner(SAS EM) 等統計軟體以及文字雲軟體進行巨量資料之採礦分析，培養學生從中發掘暨整合資訊的能力。This course is concerned with data mining, which is the application of the methods of statistics, data analysis and machine learning algorithms to the exploration and analysis of large data sets, with the aim of extracting new, previously unknown, and potentially useful information from the process of knowledge discovery in databases (KDD). Data mining is being applied in an increasing variety of areas, such as financial, economic, high-tech industrial, scientific and medical fields, as an essential component of decision assistance system. Students will learn the ability to analyze massive and complicated data and will be able to turn the raw data into valuable information using the software SAS Enterprise Miner (EM) and Enterprise Guide (EG). The objective of this course is to introduce statistical data mining concepts, describe methods in statistical data mining from sampling to decision trees, and provide decision support solutions.The following types of analyses can be performed using data mining software:
* Word Clouds
* Visual Analytics
* Descriptive methods
* Association Rule
* Cluster analysis
* Supervised learning: Predictive methods
* Multiple regression models
* Logistic regression models
* Model validation techniques
* Supervised learning: Classification
* Decision tree methods
This course provides large data-oriented thinking from the perspective of statistical and visual analysis, and links to cluster analysis, decision trees, neural networks, and rebirth/Rogis rebirth prediction models and other data mining methods. Through SAS Visual Analytics, SAS Enterprise Guide (SAS EG) and SAS Enterprise Miner (SAS EM) The same-state software and text cloud software conduct a huge amount of data mining analysis to cultivate students' ability to discover and integrate information. This course is concerned with data mining, which is the application of the methods of statistics, data analysis and machine learning algorithms to the exploration and analysis of large data sets, with the aim of extracting new, previously unknown, and potentially useful information from the process of knowledge discovery in databases (KDD). Data mining is being applied in an increasing variety of areas, such as financial, economic, high-tech industrial, scientific and medical fields, as an essential component of decision assistance system. Students will learn the ability to analyze massive and complicated data and will be able to turn the raw data into valuable information using the software SAS Enterprise Miner (EM) and Enterprise Guide (EG). The objective of this course is to introduce statistical data mining concepts, describe methods in statistical data mining from sampling to decision trees, and provide decision support solutions.The following types of analysts can be performed using data mining software:
* Word Clouds
* Visual Analytics
*Descriptive methods
* Association Rule
* Cluster analysis
* Supervised learning: Predictive methods
* Multiple regression models
* Logistic regression models
* Model validation techniques
* Supervised learning: Classification
* Decision tree methods
1. 曾淑峰、林志弘、翁玉麟（2012年9月），資料採礦應用—以SAS Enterprise Miner為工具，梅霖文化事業有限公司 (ISBN: 978-986-6511-60-8)
2. Slaughter, S.J. and Delwiche, L.D., 蔡宏明、蔡秉諺譯（2011年11月），SAS Enterprise Guide實用工具書，梅霖文化事業有限公司 (ISBN: 978-986-6511-58-5)
3. Hastie, Tibshirani, Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2ed., pringer, 2009. (全華).
4. Tan, Steinbach and Kumar, Introduction to Data Mining, Addison Wesley, 2006. (歐亞)
1. Zeng Shufeng, Lin Zhihong, Weng Yulin (September 2012), data mining application—using SAS Enterprise Miner as a tool, Meilin Culture Industry Co., Ltd. (ISBN: 978-986-6511-60-8)
2. Slaughter, S.J. and Delwiche, L.D., Cai Hongming and Cai Bing-san (November 2011), SAS Enterprise Guide practical tools book, Meilin Cultural Affairs Co., Ltd. (ISBN: 978-986-6511-58-5)
3. Hastie, Tibshirani, Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2ed., kindergartener, 2009. (All China).
4. Tan, Steinbach and Kumar, Introduction to Data Mining, Addison Wesley, 2006. (Europe)
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 作業+上機出席作業+上機出席 Work + attendance |
40 | Homeworks (單元資料庫實作)＋微電影 |
| 期中報告(個人型式)期中報告(個人型式) Midterm report (personal type) |
30 | Midterm Report (整合性報告)--PPT口頭報告+Word書面報告 |
| 期末考試(團隊型式)期末考試(團隊型式) Final exam (team type) |
30 | Final Report (總複習+創新實作論文一篇)--PPT口頭報告+Word書面報告 |
