Simon Fraser University
Engaging the World

Course Outlines

Fall 2017 - CMPT 741 G100

Data Mining (3)

Class Number: 7115

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 5 – Dec 4, 2017: Mon, 11:30 a.m.–12:20 p.m.
    Burnaby

    Sep 5 – Dec 4, 2017: Wed, Fri, 11:30 a.m.–12:20 p.m.
    Burnaby

Description

CALENDAR DESCRIPTION:

The student will learn basic concepts and techniques of data mining. Unlike data management required in traditional database applications, data analysis aims to extract useful patterns, trends and knowledge from raw data for decision support. Such information are implicit in the data and must be mined to be useful.

COURSE DETAILS:

Data mining aims to extract useful patterns, trends and previously unknown knowledge from raw data for decision support. This course has two focuses: basic concepts and techniques, and recent technologies and developments in dealing with very large data sets. For the first focus, we will study the classic data mining techniques including association, classification, and clustering; for the second focus, we will study the dominant software systems and algorithms for coping with Big Data. Topics include large-scale non-traditional data storage frameworks including graph, recommendation algorithms; and data security. The course will involve hands-on programming assignments and projects.

Topics

  • 1. Introduction
  • 2. Association Rule Mining
  • 3. Classification and Supervised Learning
  • 4. Clustering and Unsupervised Learning
  • 7. Finding Similar Items
  • 9. Link Analysis
  • 10. Recommendation Systems
  • 11. Dimensionality Reduction
  • 12. Data Security

Grading

NOTES:

Assignments/Projects (40%), Midterm (20%), and Final exam (40%)

Materials

REQUIRED READING:

Introduction to Data Mining
Pang-Ning Tan
Addison Wesley, 2006
Available online
ISBN: 9780321321367

Lecture notes: a combination of the notes provided by the authors in item 1, the slides of the course “CS345A: data mining” at Stanford University, and the slides of the instructor.,

Mining of Massive Datasets, Anand Rajaraman, Jure Leskovec, and Jeffrey Ullman
Cambridge University Press, 2012
 Available free online: http://proquest.safaribooksonline.com/9781316147047?uicode=simonfraser
ISBN: 9781107077232

Data Mining: Concepts and Techniques, 3rd Edition
Han, Kamber, Pei 
Morgan Kaufmann, 22 Jun 2011
Available online
ISBN: 9780123814791

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

SFU’s Academic Integrity web site http://students.sfu.ca/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS