Simon Fraser University
Engaging the World

Course Outlines

Fall 2026 - CMPT 455 D100

Data Mining (3)

Class Number: 4898

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 9 – Dec 6, 2026: Tue, 12:30–2:20 p.m.
    Burnaby

    Sep 9 – Dec 6, 2026: Fri, 12:30–1:20 p.m.
    Burnaby

  • Prerequisites:

    CMPT 354 and STAT 271.

Description

CALENDAR DESCRIPTION:

Data mining is the efficient discovery of useful patterns in large datasets and is a key area in big data analytics. This course focuses on fundamental data mining tasks and algorithms as well as key applications. Topics include data processing, classification techniques, cluster analysis and outlier detection. Students with credit for CMPT 459 under the title "Data Mining" may not take this course for further credit.

COURSE DETAILS:

Learning outcomes
Data Mining is 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 students with skills for developing their own data mining application and for starting their data mining research.

COURSE-LEVEL EDUCATIONAL GOALS:

Weekly Course Delivery Syllabus
Week 1:   Introduction / Data Preprocessing
Week 2:  Data Preprocessing
Week 3:  Data Preprocessing / Classification
Week 4:  Classification
Week 5: Classification
Week 6: Classification / Cluster analysis
Week 7: Cluster analysis / Midterm
Week 8: Cluster analysis
Week 9: Outlier detection
Week 10: Frequent pattern mining
Week 11: Frequent pattern mining
Week 12: Impact of data mining
Week 13: Research issues / Outlook
Course Project

In this project, student groups will explore a dataset of their choice, performing multiple data mining tasks including data preprocessing, exploratory data analysis (EDA), clustering, outlier detection, feature selection, classification, and model evaluation. The goal is to develop a comprehensive understanding of the data mining process by applying these techniques and critically analyzing the results. For this project, students are required to use the Python programming language and may utilize any Python libraries that they find beneficial to achieve the desired outcomes.
The chosen dataset must be a real-world dataset with at least 1000 samples and 10 or more features from public repositories like Kaggle, UCI Machine Learning Repository, etc. The dataset should contain a mix of numerical and categorical features, as well as at least one target variable for classification.

Grading

NOTES:

Grading
  • Assignment 1     10%
  • Assignment 2     10%
  • Midterm exam   20%
  • Course project report 20%
  • Final exam            40%

Materials

REQUIRED READING:

Data Mining: The Textbook
Charu Aggarwal
Springer
2015
ISBN: 9783319141411

Department Undergraduate Notes:

The following are default policies in the School of Computing Science. Please check your course syllabus whether the instructor has chosen a different policy for your class, otherwise the following policies apply.

  • Students must attain an overall passing grade on the weighted average of exams in the course in order to get a D or higher.

 

  • All student requests for accommodations for their religious practices must be made in writing by the end of the first week of classes, or no later than one week after a student adds a course. After considering a request, an instructor may provide a concession or may decline to do so. Students requiring accommodations as a result of a disability can contact the Centre for Accessible Learning (caladmin@sfu.ca).

 

  • Use of AI tools by students for completing graded scholarly activities and course submissions that has not been explicitly authorized by the instructor  will be considered as plagiarism under SFU’s Code of Academic Policy (http://www.sfu.ca/policies/teaching/t10-02.htm).  Following this policy, instructors will report all instances of plagiarism to the academic integrity board.  

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.