资管硕一甲.doc

資管碩一甲資料探勘Spring 2006, Thur. 9:10~12:00am (B320)
Instructor
Jinn-Yi Yeh, .(葉進儀), Room: A817, Tel: (05)273-2899, Fax: (05)273-2893,
Email: ******@, Office Hours: Thusday and Wednesday, 10:00-12:00am.
Required Text
Witten, . and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd Ed., Morgan Kaufmann, 2005.
Supplementary Texts
Han, J. and M. Kamber, Data Mining—concepts and techniques, 2001, Morgan Kaufmann, NY.
Kantardzic, M., Data Mining—Concepts, Models, Methods, and Algorithms, 2002, Wiley- Interscience, NJ.
丁一賢、陳牧言,資料探勘,滄海書局,2005
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 of a 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 Mining software.
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