Spring 2022 - CMPT 741 G100
Data Mining (3)
Class Number: 5556
Delivery Method: In Person
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.
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.
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
- Assignments/Projects 40%
- Midterm 20%
- Final Exam 40%
Introduction to Data Mining 2nd Edition, Pang-Ning Tan, Addison Wesley, Available online
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
Data Mining: Concepts and Techniques, 3rd Edition, Han, Kamber, Pei , Morgan Kaufmann, 22 Jun 2011, 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.
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 SPRING 2022
Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place. Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes. You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).
Enrolling in a course acknowledges that you are able to attend in whatever format is required. You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware 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.
Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (firstname.lastname@example.org or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.