Data Mining
EECS-6412
Fall 2018
York University

Semester: Fall 2018
Course/Sect#: EECS 6412
Time: Mon 11:30am - 1:00pm
Wed 11:30am - 1:00pm
Location: BERG 313 (Mondays)
BERG 211 (Wednesdays)
Instructor: Aijun An
Office: LAS 2048
Office Hours: Mondays: 1:00pm-2:00pm
Phone #: 416-736-2100 x44298
e-mail: aan@cse.yorku.ca

Welcome to the Data Mining course, EECS-6412, for Fall 2018. Materials, instructions, and notices for the course will accumulate here over the semester.

Message Board

January 21, 2019
Marks are posted. Please see your mark breakdown at here. Please login with your EECS account credentials.
December 20, 2018
Project presentation schedule is posted.
December 18, 2018
Please be reminded that project presentations will take place on Friday at 1:00pm in room LAS 3033. Each project has 5-8 minutes for the presentation including the question time. The final project report and programs are due December 23 at 11:00pm.
December 11, 2018
Some sample questions are posted here.
November 14, 2018
The final exam is scheduled for Monday December 17 at 9:00-11:30am in VH 3006.
Novemver 14, 2018
Project requirements and some sample course projects from previous years are posted. See the links below in the Project section.
November 7, 2018
Paper presentation schedule is posted.
November 4, 2018
An FAQ page for A3 is set up. Please see A3 Frequently Asked Questions.
November 1, 2018
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 24, 2018
Assignment 3 is posted. See the link below in the "Assignments" section.
October 6, 2018
Assignment 2 is posted. See the link below in the "Assignments" section.
September 18, 2018
Assignment 1 is posted. See the link below under "Assignments".
September 4, 2018
This web site is set up. Welcome to the course! The time and location of the first lecture will at 11:30am-1:00pm on Wednesday September 5 in BERG 211. Please note the change in class time and location. The new lecture time is MW 11:30am - 1:00pm, and the new locations are BERG 313 (Mondays) and BERG 211 (Wednesdays).

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, 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

Grading Scheme

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

Lecture Notes

Assignments

  • Assignment 1 (8%) (Due Tuesday October 2 by 6:00pm) Please note that you need a user name and a password to access the assignment. The user name and password have been emailed to you.
  • Assignment 2 (6%) (Due Monday October 22 by 6pm). The input.txt file for Question 4 can be downloaded here
  • Assignment 3 (11%) (Due Friday November 9 by 11:00pm)

Paper Review and Presentation

Project

Useful On-line Information