资管硕一甲.doc

資管碩一甲資料探勘 Spring 200 6,T hur. 9:10~12:00am ( B320 ) Instructor Jinn-Yi Yeh, . (葉進儀), Room: A817, Tel : (05)27 3-2899, Fax: ( 05)2 73-2893, Email: ******@ , Office Hours: Thusday and Wednesday, 10 :00- 12:00a m. Required Text Witten ,I. H. and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques ,2 nd Ed. , Morgan Kaufmann, 2005 . Supplementary Texts 1. Han , J. and M. Kamber, Data Mining — concepts and techniques, 2001, Morgan Kaufmann , NY. 2. Kantardzic, M., Data Mining — Concepts, Models, Methods, and Algorithms, 2002, Wiley- Interscience , NJ. 、陳牧言,資料探勘,滄海書局, 2005 4. Related research papers Description Data Mining studies algorithms putational paradigms that puters to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element ofa more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical, pattern recognition and machine learning techniques, and reporting and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process by examples of practical applications. The students will use recent Data Min