Data Mining
CSE-4412
Winter 2012
York University

Semester: Winter 2012
Course/Sect#: CSE-4412
Time: Tue 10:00pm-11:30pm
Thu 10:00pm-11:30pm
Location: VH 3006
Instructor: Aijun An
Office: CSE 2048
Office Hours: Tuesdays and Thursdays 2:00-3:00pm
Phone #: 416-736-2100 x44298
e-mail: aan@cse.yorku.ca

Welcome to the Data Mining course, CSE-4412, for Winter 2012. Materials, instructions, and notices for the course will accumulate here over the semester.

Message Board

April 26, 2012
Grades are posted. You can check yours by using ePost.
March 15, 2012
Project is posted. Please see the link below in the Assignments and Project section.
March 8, 2011
Midterm marks are posted. You can see yours using ePost.
March 7, 2012
An FAQ page for Assignment #2 is created. Please see here.
March 2, 2012
Assignment #2 is posted. Please see the link below in the Assignments and Project section.
February 22, 2012
Please be reminded that the midterm test will be held on Tuesday February 28 in class. For sample test questions, click here. The username and password are the same as the ones used for accessing the lecture notes.
January 27, 2012
An error in the description of Question 5 in Assignment 1 is corrected. Please read your email. Namely, "Slide 22" should be "Slide 27" in that question.
January 17, 2012
Assigment #1 is posted. Please see the link below in the Assignments and Project section. The access to the assignment is password-protected. The username and password are the same as the ones you use for downloading the lecture notes.
January 2, 2012
This web page is set up. Welcome to the course!

Description

Data mining or knowledge discovery from databases (KDD) is one of the most active areas of research in databases. It is at the intersection of database systems, statistics, AI/machine learning, and data visualization. In this course, we will introduce the concepts of data mining and present data mining algorithms and applications. Topics include association rule mining, sequential pattern mining, classification models, clustering, and text mining.

Prerequisites

  • Required: an introductory course on database systems.
  • Preferred: basic concepts in probability and statistics.

Materials

  • Textbook
  • Reference Books
    • Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley, 2006.
    • Ian H. Witten and Eibe Frank, Data Mining -- Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005.
    • S.M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998.
    • Margaret H. Dunham, Data Mining -- Introductory and Advanced Topics, Prentice Hall, 2003.
    • Some conference/journal papers (will be posted over the semester).

Grading Scheme

  • Assignments (25%)
  • Midterm (20%)
  • Project (20%)
  • Final exam (35%)

Lectures

Assignments and Project

Useful On-line Information
Academic Honesty