DATA MINING
             CMPS/EECE 566     Spring 2008     

 



Instructor: Dr. Vijay Raghavan

Office:  ACTR 305
Office Hours:
       Tue: 5:00 - 6:30pm;
       Wed: 10:30am - 12:00noon.
Phone:   (337) 482-6603
E-mail:  raghavan@cacs.louisiana.edu
Teaching Assistant: Shixian Chu

Room: ACTR 327 

Office Hours: 1:00 PM to 3:00 PM Mon & Wed

E-mail: shixianchu@gmail.com


                  Time & Place: 3:30 - 4:45 PM , TR, ACTR 101




Roster

Click here to check the class roster

Please check and let me(TA) know if your name is not in the roster.


Prerequisites

CMPS 460 or equivalent and Graduate Standing in Computer Science or Computer Engineering.


Outline

A study of data mining and knowledge discovery in databases.  Topics:  model representation, model evaluation, and search methods in data mining algorithms; fundamental issues in knowledge discovery; classification and clustering; trend and deviation analysis; dependency derivation; integrated discovery systems; augmented database systems; applications.

Text

Data Mining:  Concepts and Techniques by Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2nd Edition.


Policies on Cheating

Cheating: It should be strictly noted that any sort of cheating will NOT be tolerated. All work you submitted must be entirely your own. If any student is found cheating in an assigment (either programming or non-programming), he/she will be given a 0 for that assignment. This includes both the person showing their work and the person involved in copying. If any student is found cheating in a test, he/she will be given either a grade of 'C' or 'F' or in some cases will also be brought to the attention of Dean (Again includes both the person showing their work and the person involved in copying).


References

  • Data Mining by Adriaans and Zantinge, Addison-Wesley, 1996.
  • Predictive Data Mining by Weiss, Morgan Kaufmann, 1997.
  • Knowledge Discovery in Databases by Wuthrich, The Hong Kong University of Science and Technology, 1996.
  • Advances in Knowledge Discovery and Data Mining by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy, AAAI Press/The MIT Press.
  • Data Mining The Search for Knowledge in Databases by Holsheimer and Siebes, Report CS-R9406, ISSN 0169-118X, CWI.
  • OLAP Solutions Building Multidimensional Information Systems by Erik Thomsen, ISBN:   0-471-14931-4.
  • Introduction to Data Mining, First Edition,by Pang-Ning Tan, Michael Steinbach, Vipin Kumar, ISBN-13: 978-0321321367
Note: Most of the above materials will be placed in reserve at Dupre Library. Lots of relevant materials can be obtained from the Internet: http://www.kdnuggets.com/ or www.cs.bham.ac.uk/~anp/sites.html or http://www.autonlab.org/tutorials/

A couple of copies of some of the references have been placed in reserve section of the library:

i)   Reference #1: pp. 37 - 55
ii)  Reference #4: Chapter 1, pp.1-34
iii) Reference #7: pp. 19 -29

kdd explorations

Grading Policy

  • Term Project - 20-30%*
  • Homework Assignments - 30%
  • Term Test - 10-15%
  • Final Exam - 30-40%
*Typically, a term project involves the design and implementation of search and data mining algorithms or interface requirements or other knowledge discovery system components.


Class Notes


Assignments


Useful Links for Projects


Term Test




Last updated: March 17, 2008