Faculty/School

Faculty of Science

School of Mathematical Sciences

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Primary Supervisor

Associate Professor Gentry White
Position
Associate Professor
Division / Faculty
Faculty of Science

Other QUT supervisors

Professor Helen Thompson
Position
Professor
Division / Faculty
Faculty of Science

External supervisors

  • Summer Wang, Australian Bureau of Statistics
  • Lyndon Ang, Australian Bureau of Statistics

Overview

Machine learning cluster methods are common classification methods, but methods for assessing performance are limited as are methods for explaining how they work.  Exploring methods for both assessing and explaining performance are the subject of this research with application to real-world contexts with the Australian Bureau of Statistics.

Research engagement

Critical Literature Review

Research activities

  • Discuss and recommend performance metrics for cluster analysis. Identify performance metrics that are method-agnostic (i.e., are applicable to all clustering methods and can be used to compare clustering methods).
  • Discuss and recommend explainability methods for cluster analysis.
  • Recommend other criteria for assessing the quality of the results from cluster analyses.
  • Testing can be done on ABS microdata.

Research skills

Critical Thinking, ML and AI literacy

Outcomes

A critical review of the state-of-the-art in cluster analysis with potential reccomendations for the ABS

Skills and experience

A background in mathematics, statistics, or data or computer science are recommended.

Start date

2 November, 2026

End date

19 February, 2027

Location

GP Y Block Level 8

Keywords

Contact

Gentry White

3138 1658

whiteg5@qut.edu.au