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Syllabus: ISQS 6347, Spring 2007 Data & Text Mining Home | Schedule | Sign-up/Update | Students
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Schedule: MWF 11:00-12:50p, BA 363 (Lab) or BA 268 (Sometimes for
lectures)
Instructor: Zhangxi Lin, (806) 742-1926, BA 708; Office hours: M, WTh 1:30-3:30p, or by appointment.
Email: zhangxi.lin@ttu.edu, MSN: Zhangxi@sbcglobal.net, zhangxi.lin@hotmail.com, Google talk
ID: zhangxi.lin
Course Description:
This course covers the basics of data mining and text mining, with
applications in business intelligence, customer relationship management, fraud and terrorism detection, improvement of resource
utilization, clickstream web mining, and credit
scoring for loan applications. The
software SAS Enterprise Miner will be used extensively to illustrate use of
decision trees, classification algorithms, neural nets, clustering, and other
data and text mining techniques.
Participants in this course are eligible to receive a data
mining certificate from SAS Institute and
Learning objectives:
- Understanding
the general principles of data mining
- Being
able to apply the commonly used functions of SAS Enterprise Miner to solve
data mining problems
- Developing
the skills of data mining modeling and data analysis with SAS Enterprise
Miner
- Mastering
general data preparation skills and tools
Prerequisites: A basic statistics course, such as ISQS 5345 “Statistical Concepts for
Business & Management” or ISQS 5347 “Advanced Statistical Methods” (B or
better), or equivalent; Programming, SAS, and/or Database are helpful but not
required.
Textbook:
Required: Data
Mining, Roiger and Gertz.
3rd edition. Addison Wesley, ISBN 0201741288 (This book comes with a Microsoft
Excel based data miner called iData Analyzer).
Optional:
1) Introduction to Data Mining,
Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison Wesley, 2005,
ISBN: 0321321367 (Website: http://www-users.cs.umn.edu/~kumar/dmbook/index.php
)
2) Data
Mining for Business Intelligence: Concepts, Techniques, and Applications in
Microsoft Office Excel with XLMiner, Galit Shmueli, Nitin R. Patel,
Peter C. Bruce, ISBN: 978-0-470-08485-4, Hardcover, 279 pages, December 2006
3) Introduction to Data Mining - Using SAS
Teaching style: Case-based hands-on learning process
Deliverable and
Grading Policy:
- Six
quizzes out of seven (30 points)
- In-class
exercises (120 points)
- Homework
(30 points)
- Midterm
project (40 points)
- Term
project (60 points)
The total is 280 points.
Projects:
All projects will be conducted in the basis of group. Each
project group consists of 2-3 students.
References:
- Dr. Peter
Westfall’s data mining class (Fall 2004)
- Principles
of Data Mining, David J. Hand, Heikki Mannila and Padhraic Smyth,
The MIT Press, August 2001, ISBN 0-262-08290-X, 425 pp.
- Data Mining: Concepts and
Techniques, Jiawei Han, Micheline Kamber, Morgan Kaufmann, 2000, ISBN: 1558604898
- Data
Mining Using SAS
- Online
SAS references
- SAS
documentation (authorized persons only – please do not distribute)
- Selected
online resources:
·
StatLib: http://lib.stat.cmu.edu/
·
MLnet: http://www.mlnet.org/
·
KDNuggets: http://www.kdnuggets.com/
·
Weka: http://www.cs.waikato.ac.nz/ml/weka/
·
Open
source data mining projects: http://www.kdkeys.net/forums/72/ShowForum.aspx
·
Open
source data mining tools: http://dmoz.org/Computers/Software/Databases/Data_Mining/Public_Domain_Software/
Outline of the Course
I. DATA MINING FUNDAMENTALS
1.
Data Mining Concepts
2. SAS
Enterprises Miner
II. Data Mining
Techniques
3.
Decision Trees.
4.
Logistic Regression
5.
Neural Network
6.
Association Analysis.
7.
Clustering
III. TEXT MINING
8.
Preliminaries
9.
Exploratory Analysis of Document Collections
10.
Predictive Modeling
IV. WEB MINING
11. Introduction
12. Data collection for web
mining
13. Knowing customers
14. Attracting web visitors
15. Evaluating web visitors
16. Keeping customers