Data Mining
EECS-4412
Fall 2017
York University

Semester: Fall 2017
Course/Sect#: EECS-4412
Time: Tue 1:00pm-2:30pm
Thu 1:00pm-2:30pm
Location: SC 222
Instructor: Aijun An
Office: LAS 2048
Office Hours: Tue: 2:45-3:45pm
Wed: 4:15-5:15pm
Phone #: 416-736-2100 x44298
e-mail: aan@cse.yorku.ca

Welcome to the Data Mining course, EECS-4412, for Fall 2017. Materials, instructions, and notices for the course will accumulate here over the semester.

Message Board

December 4, 2017
The due time for the project is extended to tonight at 11:59pm.
November 19, 2016
Project is posted. Please see the link below in the Assignments and Project section.
November 16, 2016
The due time for Assignement 3 is extended to 11:59pm tonight.
November 9, 2016
Midterm solutions are posted. Click here to download.
November 9, 2016
Midterm marks are posted. You can check yours by using ePost.
November 9, 2016
An FAQ page for Assignment #3 is created. Please see here.
November 3, 2017
Assignment #3 is posted. Please see the link below in the Assignments and Project section.
November 2, 2017
Solutions to Sample Midterm Questions are posted. Please see here.
November 1, 2017
Solutions to Assignment #2 are posted. Please see here.
October 27, 2016
Please be reminded that the midterm test will be held on Thursday November 2 at the class time in ACE 005. Note that the location is different from our regular classroom. For sample test questions, click here. The username and password are the same as the ones used for accessing the lecture notes.
October 12, 2017
Assignment #2 is posted. Please see the link below in the Assignments and Project section.
September 24, 2017
Assigment #1 is posted. Please see the link below in the Assignments and Project section. The access to the assignment is password-protected. The username and password are the same as the ones for accessing the lecture notes.
September 6, 2017
This web page 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, clustering, and text mining.

Prerequisites

  • Required: a course on data structures and an introductory course on database systems.
  • Preferred: basic concepts in probability and statistics.

Materials

  • Textbook
      Jiawei Han, Micheline Kamber and Jian Pei, Data Mining - Concepts and Techniques, Morgan Kaufmann, Third Edition, 2011.
  • Reference Books and Materials
    • Charu C. Aggarwal, Data Mining, The Textbook, Springer, 2015.
    • 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.
    • Some conference/journal papers
    • More books can be found here

Grading Scheme

  • Assignments (25%)
  • Midterm (20%) (Thursday November 2 at the class time in ACE 005)
  • Project (20%)
  • Final exam (35%)

Lectures

Assignments and Project

  • Assignment 1 (Weight: 8%) (Due Tuesday October 10 in class)
  • Assignment 2 (Weight: 6%) (Due Wednesday October 25 by 10pm. Please submit a pdf file online here
  • Assignment 3 (11%) (Due Thursday November 16 by 11:59pm).
  • Project (20%) (Due Monday December 4 at 11:59pm)
Useful On-line Information
Academic Honesty