Simon Fraser University
Engaging the World

Course Outlines

Fall 2020 - CMPT 741 G100

Data Mining (3)

Class Number: 6670

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 9 – Dec 8, 2020: Tue, 1:30–3:20 p.m.
    Burnaby

    Sep 9 – Dec 8, 2020: Thu, 1:30–2:20 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 finding similar items, link analysis, recommendation algorithms, data privacy and security. The course will involve assignments/projects, one midterm and final exam.

Topics

  • 1. Introduction
  • 2. Association Rule Mining
  • 3. Classification and Supervised Learning
  • 4. Clustering and Unsupervised Learning
  • 5. Finding Similar Items
  • 6. Link Analysis
  • 7. Recommendation Systems
  • 8. Data Privacy and Security

Grading

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

Materials

REQUIRED READING:

  • Introduction to Data Mining 2nd Edition, Pang-Ning Tan, Addison Wesley, 9780133128901, Available online
  • 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., , Will be available online to enrolled students.
  • Mining of Massive Datasets, Anand Rajaraman, Jure Leskovec, and Jeffrey Ullman, Cambridge University Press, 2012, 9781107077232, Available free online: http://proquest.safaribooksonline.com/9781316147047?uicode=simonfraser
  • Data Mining: Concepts and Techniques, 3rd Edition, Han, Kamber, Pei , Morgan Kaufmann, 22 Jun 2011, 9780123814791, Available online

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:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/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

TEACHING AT SFU IN FALL 2020

Teaching at SFU in fall 2020 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).