Fall 2020 - CMPT 459 D100

Special Topics in Database Systems (3)

Data Mining

Class Number: 7064

Delivery Method: In Person


  • Course Times + Location:

    Tu 11:30 AM – 1:20 PM

    Th 11:30 AM – 12:20 PM

  • Prerequisites:

    CMPT 354.



Current topics in database and information systems depending on faculty and student interest.


This course introduces Data Mining, an area that plays a key role in Big Data analytics. The goal of data mining is the efficient discovery of useful patterns in large datasets. This course focuses on fundamental data mining tasks and algorithms as well as key applications. It will prepare you both for developing your own data mining application and for starting your data mining research. Students taking this course are expected to have taken an algorithms course and to have an understanding of basic statistics equivalent to an entry-level course. The course project requires programming in Python or R, and students are expected to be proficient with one of these programming languages.


  • Introduction
  • Data preprocessing: data cleaning, completion, transformation, normalization
  • Classification: evaluation, decision trees, Bayesian classification, NN, SVM, ensemble methods
  • Cluster analysis: partitioning, hierarchical, density-based methods, subspace clustering
  • Outlier detection: probabilistic and distance-based methods, LOF, non-parametric methods
  • Frequent pattern mining: association rules, Apriori, FP-growth, pattern summarization
  • Applications: social network analysis, recommender systems, precision medicine
  • Research issues: active learning, causal discovery, explainability, transfer learning


  • Evaluation will be based on paper and pencil assignments, a course project, and a final exam. If the teaching will be online in the fall, the exam will be a take-home exam. Details to be discussed and finalized in the first week of classes. Students must attain an overall passing grade on the weighted average of exams in the course in order to obtain a clear pass (C- or better).



Data Mining: The Textbook., Charu Aggarwal, Springer, 2015,, The book is available as e-book through the SFU Library.
ISBN: 9783319141411

Registrar Notes:


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 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).