資料探勘與商業智慧

臺北科技大學 115-1 學期課程，課號 363987。

課程大綱

建立學生對各類資料探勘技術與理論的基礎觀念及架構，以奠定學生日後從事資料探勘分析與研究工作時之需。相關的分析技術將包括應用在分類(classification)、推估(estimation)、預測(prediction)、關聯式分組(affinity grouping)、同質分組(clustering)等功能中的決策樹、(decision tree)多變量分析(multivariate analysis)及類神經網路(neural network)等方法。此外為了讓學生對相關技術有更深的瞭解，課程中將以個案實例為主，並配合各種資料探勘套裝軟體，以應用的觀點使學生更容易接受了解在商業智慧分析上資料探勘分析的功用。 Several fundamental theories of data mining techniques are included in this course. For examples, decision tree, multivariate analysis and neural network. All these techniques will be applied in different areas, such as classification, association rule, forecasting, and pattern recognition. And various applications in different enterprises are also included.

課程進度

1 (09/07) Introduction 2 (09/14) Illustrated Examples and BI 3 (09/21) Cluster analysis 1 4 (09/28) 教師節 5 (10/05) Cluster analysis 2 6 (10/12) Discriminant 1 7 (10/19) Discriminant 2 8 (10/26) 補假 9 (11/02) Discriminant 3 10 (11/09) Exam 1 11 (11/16) Neural Network (MLP) 12 (11/23) Decision Tree 13 (11/30) Association Rule 14 (12/07) Exam 2 15 (12/14) Project discussion 16 (12/21) Project discussion

評量方式

Exam 1&2 60%(=2*30%) Final Project 40%

教材

【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：否 ● 資料探勘 (Data Mining), 曾憲雄等, 旗標出版股份有限公司, 2005 ● Machine learning, Tom Mitchell, McGraw-Hill, 1997 ● Data Mining: Concepts and Techniques, Han and Kamber, Morgan Kaufmann, 2000 ● Predictive Data Mining, Weiss and Indurkhya, Morgan Kaufmann, 1998 ● Data Mining Your Web Site, J. Mena, Digital Press, 1999 ● Research paper collection, (collected by instructor) will be distributed in class ● Applied Multivariate Statistical Analysis, by Johnson, R.A., and Wichern, D.W., Prentice Hall, 2002. ● Machine Learning Group at the University of Waikato https://www.cs.waikato.ac.nz/ml/weka/courses.html