Spring 2023 - CMPT 741 G100
Data Mining (3)
Class Number: 6531
Delivery Method: In Person
Course Times + Location:
We 2:30 PM – 3:20 PM
SSCK 9500, Burnaby
Fr 2:30 PM – 4:20 PM
AQ 3159, Burnaby
1 778 782-4667
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
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
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 website 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