CSE 572 DATA MINING
(January 17 - May 2, Spring 2006)
I hear, I forget; I see, I remember; I do, I understand. -
Proverb
We will hear, see and do in this class in many forms including
lectures,
invited talks, discussions, research paper reading assignment, a
project, and presentation, in addition to homework, quizzes, and
exam(s). We will create opportunities to learn from each other.
If you find anything interesting about data mining, please share with
all.
Your suggestions are most welcome. Please send email to hliu at asu.edu
OUTLINE
- Introduction
to Data Mining
- Data, data preparation, and data
Preprocessing (feature selection, discretization, sampling, instance
selection)
- A Brief Review of Probability and
Entropy
- Classification Methods (ensemble methods,
skewed data, cost-sensitive classification)
- Performance Evaluation (Measures,
Comparison)
- Clustering Methods (subspace clustering,
CLIQUE)
- Association Rules
- Some Applications (steganography and steganalysis, streaming data
extraction, gene selection)
ASSIGNMENTS (To be updated)
- There is a credit (10%) for class participation and discussion
(See
the home page).
- Assignments 1 and 2 are stated in the first set of slides.
- Research paper
reading
assignment, and selected presentations (We will discuss more in class)
- Project proposal (If we have proposal presentations, there will
be 2 minutes each to share your ideas
about what you're going to do) (Project
Categories)
- Project progress report
- Project presentation
[please submit the URL links to TA], schedule will be available at myASU)
- Every student will have 15 minutes to present his project
including 2 minutes for Q/A
- 12-15 slides will be usually sufficient for this type of
presentation
- Project final report
and demo
More on Paper Reading Assignment,
Project and its Due Dates
EXAMS
LINKS
Prepared by Huan Liu
on Dec. 17, 2006
Last updated: Jan 18, 2006