​CSS3953 – Data Mining

Course Description

To provide the students with knowledge of algorithms and computational paradigms that allow the computer to find patterns and regularities in databases or large data-sets, so that the computer can be used to perform forecasting and improve the performance of interactions. ​This course provides the students with knowledge of algorithms and computational paradigms that allow the computer to find patterns and regularities in databases or large data-sets by using selection, cleaning, coding, pattern recognition, machine learning and other statistical methods, so that the computer can be used to perform forecasting and improve the performance of interactions.

​Content Outline of the Course/Module:

  • Introduction to Data Mining
  • Data Warehouse and OLAP Technologies for Data Mining
  • Data Pre-Processing
  • Concept Description
  • Mining Association Rules in Large Database
  • Classification and Prediction
  • Cluster Analysis
  • Mining, Complex Types of Data
  • Applications and Trends in Data Mining

Course Outcome

Upon completion of this course, students should be able to:

  • Revise the concept of data warehouses and online analytic processing (OLAP).
  • Construct techniques for pre-processing data and concept description.
  • Perform efficiently methods for mining association rules, data classification, prediction, and cluster analysis.

Subject Area

Elective

Course Director

Dr. Chew Kim Mey

Teaching-learning Methods ​Assessment Methods
Practical Works ​Assignment, Progress Test, Final Examination
Computer-based Learning ​Assignment, Progress Test, Final Examination
​Lecture ​Assignment, Progress Test, Final Examination
Tutorial ​Assignment, Progress Test, Final Examination