Data Mining
EECS-6412
Fall 2019
York University

Semester: Fall 2019
Course/Sect#: EECS 6412
Time: Tue 1:00pm - 2:30pm
Thu 1:00pm - 2:30pm
Location: CC 106 (Tuesdays)
VH 3009 (Thursdays)
Instructor: Aijun An
Office: LAS 2048
Office Hours: Tuesdays: 2:30pm-3:30pm
Phone #: 416-736-2100 x44298
e-mail: aan@cse.yorku.ca

Welcome to the Data Mining course, EECS-6412, for Fall 2019. Materials, instructions, and notices for the course will accumulate here over the semester.

Message Board

March 9, 2020
Marks are posted. Please see your mark breakdown at here. Please login with your EECS account credentials.
December 22, 2019
The poject due time is extended to December 29th at 11:59pm. Please see the end of the project Outline/Requirements page for how to submit the project and what to submit.
December 7, 2019
Some sample exam questions are posted here.
Novemver 19, 2019
Project requirements and some sample course projects from previous years are posted. See the links below in the Project section.
November 11, 2019
Final exam is set for 1:00pm-4:00pm on Thursday Decemver 12 in BRG 313.
November 8, 2019
The deadline for submitting Assignment #3 is extended to Wednesday November 13 at 6:00pm.
November 5, 2019
An FAQ page for A3 is set up. Please see A3 Frequently Asked Questions.
November 4, 2019
Student presentation schedule and evaluation criteria are posted. Please see the links below in the "Student Presentation" section.
October 27, 2019
Assignment 3 is posted. See the link below in the "Assignments" section.
October 10, 2019
Assignment 2 is posted. See the link below in the "Assignments" section.
September 23, 2019
Assignment 1 is posted. See the link below under "Assignments".
September 4, 2019
This web site is set up. Welcome to the course! The time and location of the first lecture will at 1:00pm-2:30pm on Thursday September 5 in VH 3009.

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 8 by 5:00pm) 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 Thursday October 24 by 11pm).
  • Assignment 3 (11%) (Due Monday November 11 by 6:00pm, extended to Wednesday November 13 at 6:00pm)

Student Presentation

Project

Useful On-line Information