Download Introduction to customer relationship management and data mining

顧客關係管理(Customer Relationship Management)
授課教師：李天行
授課時間：星期四下午六時四十分至九時三十分
辦公室：SL 407 or SL 241
辦公室開放討論時間：星期三下午一時四十分至三時三十分
辦公室聯絡電話：2905-2905 or 2905-2681
E-mail address：[email protected], [email protected]
課程目標: The objectives of this course is to give an overview about different aspects of
customer relationship management (CRM) and data mining (DM) as well as the
methodologies and applications of CRM and DM. The focus of this course will be
application oriented and therefore how to use related techniques in handling real world
problems is a must in this course. Therefore the students are required to use relevant data
mining techniques in handling real world problems in their final projects.
先修課程: The first year MBA students who have not taken multivariate statistical method
are NOT allowed to take this course. The above policy is strictly followed. No
exception will be made under any circumstances!
參考書籍：
1. Data Mining: Concepts and Techniques, Han and Kamber, Morgan Kaufmann, 2000
2. Predictive Data Mining, Weiss and Indurkhya, Morgan Kaufmann, 1998
3. Data Mining Your Web Site, J. Mena, Digital Press, 1999
4. Introduction to Data Mining and Knowledge Discovery, Two Crows, 1999
5. Research paper collection, (collected by instructor) will be distributed in class
課程含蓋章節：
●Introduction to customer relationship management and data mining
●Commonly used terminology and data mining processes
●Introduction to call centers and call center visits
●Association rules, classifications, predictions, clusters, and sequential patterns
●Case studies and discussions
●Paper discussions and presentations
實務講座及軟體教授: There should be one call center visit and one or two presentations
by data mining software providers.
計分方式: Presentation 40%, final project 40% and class participation 20%
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Projects and Presentation Related Information
Presentation:
Each group will be requested to present a paper or a case related to CRM or DM.
The papers can be prepared by the instructor or yourselves through the library or e-journal
systems. Each group will then prepare a 60-minute presentation based on the contents of
the paper or the case..
Project (more like a case study):
You have two choices for the final projects. You could either write a case
regarding the actual CRM system adopted by a company or you can choose one
application domain, and prepare the documentation for your case study including the
application case, how do you prepare for your data, choose the mining type, how would
you explain your result and what problems you might encounter for this problem? (Please
note that the written report of this project should be printed using in 12pt font, single line
spacing, and should not exceed 15 pages. Please also prepare a 30-minute presentation
about your work. The length of the essay is strictly required to be between 10 to 15 pages.
However, I will pay more attention to the quality of your essay, not just the number of
pages.)
Other useful information:
Conferences
ACM KDD
http://informatik.uni-trier.de/~ley/db/conf/kdd/index.html
ACM PAKDD
http://www.kecl.ntt.co.jp/icl/about/ave/PAKDD00/
WebKDD
http://wum.wiwi.hu-berlin.de/~myra/WEBKDD99/
Journals
Data Mining and Konwledge Discovery
http://www.wkap.nl/journalhome.htm/1384-5810
References
KDNuggets Directory: Data Mining and Knowledge Discovery
http://www.kdnuggets.com/index.html
Data mining software
http://www.cs.bham.ac.uk/~anp/software.html
Machine learning database repositories. -- University of California-Irvine
http://www.ics.uci.edu/~mlearn/MLRepository.html
Bibliography: Larry G's overview of DW & OLAP books
http://pwp.starnetinc.com/larryg/books.html
Bibliography: A research-oriented bibliography on DW and OLAP
http://www.cs.toronto.edu/~mendel/dwbib.html
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