Data Mining
CSE-6412
Fall 2009
York University

Semester: Fall 2009
Course/Sect#: CSE-6412
Time: Tue 2:30pm-4:00pm
Thu 2:30pm-4:00pm
Location: R-S537
Instructor: Aijun An
Office: CSB 2048
Office Hours: Tue and Thur: 4:00pm - 5:00pm
Phone #: 416-736-2100 x44298
e-mail: aan@cse.yorku.ca

Welcome to the Data Mining course, CSE-6412, for Fall 2009. Materials, instructions, and notices for the course will accumulate here over the semester.

Message Board

November 18, 2009
The final exam is scheduled for Monday December 14 at 1:00pm-3:00pm in CSE 3033. Project presentations will take place on Monday December 21 at 1:00-3:00pm in CSE 3033.
November 16, 2009
Paper presentation schedule is posted.
November 10, 2009
An FAQ page for A2 is set up. Please see A2 Frequently Asked Questions.
November 10, 2009
The reading list for student paper presentations is posted. See the links below in the "Paper Review and Presentation" section for the reading list and requirements for the presentation.
November 3, 2009
Assignment 2 is posted. See the link below in the "Assignments" section.
September 30, 2009
Assignment 1 is posted. See the link below under "Assignments".
September 8, 2008
The web site 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, and clustering.

Prerequisites

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

Reference Books and Materials

  • Jiawei Han and Micheline Kamber, Data Mining -- Concepts and Techniques, Morgan Kaufmann, Second Edition, 2006.
  • 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.
  • Margaret H. Dunham, Data Mining -- Introductory and Advanced Topics, Prentice Hall, 2003.
  • Some conference/journal papers (More will be posted over the semester).

Grading Scheme

  • Assignments (25%)
  • Final exam (30%)
  • Paper review and presentation (10%)
  • Course project (25%)
  • Participation (10%)

Lecture Notes

Assignments

  • Assignment 1 (12%) (Due Tuesday October 20 in class) Please note that you need a user name and a password to access the assignment. Please check your email for the user name and password.
  • Assignment 2 (13%) (Due Wednesday November 18 by 5pm)

Paper Review and Presentation

Project

Useful On-line Information