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

  1. Introduction to Data Mining
  2. Data, data preparation, and data Preprocessing (feature selection, discretization, sampling, instance selection)
  3. A Brief Review of Probability and Entropy
  4. Classification Methods (ensemble methods, skewed data, cost-sensitive classification)
  5. Performance Evaluation (Measures, Comparison)
  6. Clustering Methods (subspace clustering, CLIQUE)
  7. Association Rules
  8. Some Applications (steganography and steganalysis, streaming data extraction, gene selection)

ASSIGNMENTS (To be updated)

  1. There is a credit (10%) for class participation and discussion (See the home page).
  2. Assignments 1 and 2 are stated in the first set of slides.
  3. Research paper reading assignment, and selected presentations (We will discuss more in class)
  4. 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)
  5. Project progress report
  6. Project presentation  [please submit the URL links to TA], schedule will be available at myASU)
  7. 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