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课程摘要
由于科技的发展，导致与管理决策相关的资料，正以一种惊人的速度被不断累积。作为网际网络、电子商务、电子银行、销售点设备、条码阅读机及智慧型机器等创新的副产品，电子资料的获取，已经变得廉价而且普及。这些通常被储存于资料仓储和资料超市中，并特别准备管理决策提供支援。资料探勘是一个快速成长的领域，其主要任务在发展相关技术以协助管理者对这些数据库进行智能化的运用。而资料探勘在信用评级、诈欺揭发、营销数据库、客户关系管理及股票投资市场等方面的一些成功应用亦已被揭示。资料探勘领域已经从统计学及人工智能领域演变发展起来。
本课程将检视那些在这两个领域中出现，并被证实从应用的角度上，对于模式的辨识和预测，具有价值的方法。我们将会了解那些应用，并通过简单易用的软件及案例，提供亲自动手操作资料探勘算法的机会。
课程目标
理解当前流行的资料探勘技术之效力与局限，并能够辨明资料探勘的商业应用前景。学生们能积极地管理和参与由顾问或专家执行的资料探勘计划。从本课程中将获得额外有用的技巧，是学会使用Excel中强大的资料数据分析功能。
课堂讲稿
讲稿和指定的家庭作业，将可自课程网页SloanSpace中取得。同学们须自行下载，以进行课前预习和缴交作业。
补充阅读资料
以下书籍为课程的补充读物，而书中的材料将有可能被加进课程讲稿中。
在Dewey图书馆的藏书 :
Hand，Mannila和 Smyth。《资料探勘原理》。MIT出版社，2001。
电子媒体 :
Berry 和 Linoff。《精通资料探勘》，Wiley，2000。http://library.books24x7.com/book/id_827/toc.asp
Delmater和Hancock。《资料探勘详解》，数位印刷。2001。http://library.books24x7.com/book/id_2643/toc.asp
软件
我们将使用XLMIER，一种Excel的插件，来完成作业。欲下载免费版本可至 http://www.xlminer.com免费的版本权限有限，您将需要一种由RESAMPLING Stats所提供之功能更强的版本，以完成作业和案例。下载地址：http://www.resample.com/xlminer/MIT
在所需处理大量资料的计划中，将可利用SAS Enterprise Miner软件。对于软件的应用指南将被详细介绍。
成绩评量
您的课程成绩将取决于案例写作、家庭作业、团队研究计划、期中考试。这些成份所占比例如下：
案例写作及家庭作业(占30%)；期中考试(占30%)；研究计划(占40%)
课堂参与情况将由教师主观评量，并在成绩处于及格边缘时，用于决定最终成绩。
Course Summary
Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.
This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.
Course Objective
To develop an understanding of the strengths and limitations of popular data mining techniques and to be able to identify promising business applications of data mining. Students will be able to actively manage and participate in data mining projects executed by consultants or specialists in data mining. A useful takeaway from the course will be the ability to perform powerful data analysis in Excel.
Lecture Notes
Lecture notes and homework assignments will be available at the class website in SloanSpace. You will be responsible for downloading them to prepare for class as well as to submit home works.
Supplementary Readings
The following books are available as supplementary materials. Occasionally, readings from these books will be suggested to augment the lecture notes.
On reserve in Dewey library:
Hand, Mannila, and Smyth. Principles of Data Mining. MIT Press, 2001.
Available in electronic media:
Berry and Linoff. Mastering Data Mining. Wiley, 2000. http://library.books24x7.com/book/id_827/toc.asp
Delmater and Hancock. Data Mining Explained. Digital Press, 2001. http://library.books24x7.com/book/id_2643/toc.asp
Software
We will be using XLMiner, an Excel add-in, for homework assignments. To download a free version go to http://www.xlminer.com
The free version is limited. For your home works and case assignments you will need a more powerful version that will be provided by Resampling Stats at http://www.resample.com/xlminer/MIT
SAS Enterprise Miner will be available for projects that require handling large amounts of data. Instructions on using the software will be provided in recitations.
Grading
Your course grade will be based on case write-ups, homework, a team project and a mid-term exam. The weights given to these components is:
Case write-ups and Homework (30%); Mid-term Exam (30%); Project (40%)
Class participation will be subjectively evaluated and will be used in borderline cases to determine the final grade.
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