Data Mining
EECS-6412
Winter 2017
York University


Semester: Winter 2017
Course/Sect#: EECS 6412
Time: Mon 11:30am-1:00pm
Wed 11:30am-1:00pm
Location: CB 129
Instructor: Aijun An
Office: LAS 2048
Office Hours: Mon&Wed: 1:15-2:00pm
Phone #: 416-736-2100 x44298
e-mail: aan@cse.yorku.ca


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


Message Board

April 18, 2017
Please be reminded that project presentations will take place tomorrow April 19 at 1:00-3:30pm in room LAS 3033. Each projet has about 10-15 minutes for the presentation including the question time. Please see here for the presentation schedule.
April 8, 2017
Please be reminded that the final exam is scheduled for Wednesday April 12 at 3:00am-5:30pm in LAS 3033. You can find some sample questions here.
March 15, 2017
A list of potential course projects is posted. Please see the link to it in the Project section below. Also, in that section, you should see project requirements and some sample course projects from previous years.
March 12, 2017
Paper presentation schedule is posted.
March 7, 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.
March 3, 2017
The due date for Assignment 2 has been extended to Sunday March 12 by 11:59pm.
February 22, 2017
An FAQ page for A2 is set up. Please see A2 Frequently Asked Questions.
February 16, 2017
Assignment 2 is posted. See the link below in the "Assignments" section.
January 23, 2017
Assignment 1 is posted. See the link below under "Assignments".
January 16, 2017
Please be reminded that our lecture room has been changed to CB 129, effective today. Also, lecture notes have been put under password protection. Credentials for accessing the lecture notes have been emailed to your cse or yorku account. Please check your email.
January 8, 2017
This web site is set up. Welcome to the course! The first lecturer will be at 11:30am - 1:00pm on Monday January 9.


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
  • More books can be found here


Grading Scheme

  • Assignments (25%)
  • Final exam (30%) (Time: 3:00pm-5:30pm on Wednesday April 12. Location: LAS 3033)
  • Paper review and presentation (10%)
  • Course project (25%)
  • Participation (10%)


Lecture Notes


Assignments

  • Assignment 1 (12%) (Due Wednesday February 8 in class) 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 (13%) (Due Sunday March 12 by 11:59pm)


Paper Review and Presentation


Project


Useful On-line Information