Data Mining
Fall 2017
York University

Semester: Fall 2017
Course/Sect#: EECS 6412
Time: Mon 2:30pm-4:00pm
Wed 2:30am-4:00pm
Location: CB 120
Instructor: Aijun An
Office: LAS 2048
Office Hours: Tuesdays: 2:45pm-3:45pm
Wednesdays: 4:15-5:15pm
Phone #: 416-736-2100 x44298

Welcome to the Data Mining course, EECS-6412, for Fall 2017. Materials, instructions, and notices for the course will accumulate here over the semester.

Message Board

December 18, 2017
Please be reminded that project presentations will take place on Wednesday at 1:00pm in room LAS 3033. Each project has 10-15 minutes for the presentation including the question time. The final project report and programs are due December 22 at 11:59pm.
December 8, 2017
Please be reminded that the final exam is scheduled for Monday December 11 at 1:00am-3:30pm in LAS 3033. You can find some sample questions here.
Novemver 14, 2017
Project requirements and some sample course projects from previous years are posted. See the links below in the Project section.
November 9, 2017
An FAQ page for A3 is set up. Please see A3 Frequently Asked Questions.
November 7, 2017
Paper presentation schedule is posted.
November 3, 2017
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.
October 30, 2017
Assignment 3 is posted. See the link below in the "Assignments" section.
October 12, 2017
Assignment 2 is posted. See the link below in the "Assignments" section.
September 25, 2017
Assignment 1 is posted. See the link below under "Assignments".
September 11, 2017
This web site is set up. Welcome to the course! The first lecture will be at 2:30am - 4:00pm today.


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.


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

Reference Books and Materials

  • Jiawei Han, Micheline Kamber and Jian Pei, Data Mining -- Concepts and Techniques, Morgan Kaufmann, Third Edition, 2011.
  • 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.
  • Mohammed J. Zaki and Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, 2014.
  • Some conference/journal papers
  • More books can be found here

Grading Scheme

  • Assignments (25%)
  • Final exam (30%) (Time: 1:00pm-2:30pm on Monday December 11. Location: LAS 3033)
  • Paper review and presentation (10%)
  • Course project (25%)
  • Participation (10%)

Lecture Notes


  • Assignment 1 (8%) (Due Tuesday October 10 by 4:30pm) Please note that you need a user name and a password (that you use to download the lecture notes) to access the assignment.
  • Assignment 2 (6%) (Due Friday October 27 by 10pm)
  • Assignment 3 (11%) (Due Monday November 13 by 10:00pm)

Paper Review and Presentation


Useful On-line Information