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Daphne T. C. Lai Lecturer, Faculty of Science

About Me Publications

Daphne T. C. Lai

Lecturer, Faculty of Science


Daphne Lai is a Computer Science Lecturer at the Faculty of Science, Universiti Brunei Darussalam. Her research interests lie in the areas of data mining and artificial intelligence, ranging from applied as well as design. In recent years, she has been investigating on better techniques for cluster analysis and on machine learning applications for predictive analysis. She is collaborating with researchers in several disciplines of health care, particularly cancer registry and cardiac rehabilitation.

She currently serves on the Steering Committee for ASEAN-IVO under NICT and is also a IEEE member.

Daphne is currently lecturing in Programming Fundamentals, Computer Programming and Data Mining.

There are vacancies for interested Masters & PhD candidates, please kindly email your research proposals, academic transcripts, and publications.
Scholarship application websites:


BSc in Computer Science, University of Stratchclyde, UK (Thesis: Elliptic Curve Cryptography)
MSc in Distributed systems and Networks, University of Kent, Canterbury, UK. (Thesis: Economy of Stable Job Scheduling in Grid Computing Systems)
PhD in Computer Science, University of Nottingham, UK (Thesis: An Exploration of Improvements to Semi-supervised Fuzzy c-Means Clustering for Real-World Biomedical Data.)


Data Mining, Artificial Intelligence, Cluster Analysis, Pattern Recognition, Machine Learning, Biomedical Applications, Bioinformatics, Clinical Informatics, Data Science


Scopus Publications


Google Scholar Citations


Google Scholar h-index


Google Scholar i10-index


Machine Learning Algorithms

We are studying the following areas of machine learning algorithms: 1) Evolutionary methods for unsupervised/semi-supervised clustering 2) Metric Learning algorithms, similarity measures 3) Model-based techniques 4) Time-based clustering algorithms 5) Intelligent Data Fusion.

Application invited for: MSc/ PhD

(Automatic) Data Mining Frameworks

Data mining and machine learning are applied for discovery of patterns and for prediction in the follow domains: 1) Clinical/Biomedical datasets (a) cancer registry, b) health surveys, c) cardiac rehabilitation) 2) Open/public data 3) Driving behaviours 4) Geology

Application invited for: MSc/ PhD

Automatic Data Fusion

In this project, we develop a platform or framework that can automate the process of data fusion (integration and feature engineering) of data set from various public sources and data abstraction for knowledge discovery and predictive analysis.

Application invited for: MSc


Grant type: URG, Grant Number: 311, Project Title: Exploring metaheuristics in a semi-supervised fuzzy c-means (ssFCM) clustering framework applied on biomedical data, Investigators (PI/Co-PI): Daphne TC Lai, Funding Details: BND 9,231, Start Date: 01/03/2015, End Date: 29/02/2018


1. Best Abstract and Best Poster for The Cardiac Society of Brunei Darussalam’s (CSBD) second Annual Scientific Meeting 2015, Brunei for work titled: EVALUATE THE EFFECT OF LONG-TERM COMPREHENSIVE CARDIAC REHABILITATION IN CORONARY ARTERY DISEASE: A COHORT STUDY by authors: SK Jong, DTC Lai, SK Ong and CL Chong.


Consultancy in Data Analytics, Data Mining and Machine Learning is available.
Interested parties may email at daphne.lai[[AT]]
Happy to chat on the application and potential of DM and ML to businesses/industries.

Previous consultancy:
1. PHP 5 for Ministry of Development staff (16 – 18 and 23 – 26 March 2009)
2. FLOSS for local and CLMV (Cambodia-Laos-Myanmar-Vietnam) teachers and system administrators under the Initiative for ASEAN Integration Programme (3 - 5 March 2009)
3. Cascading Style Sheets for Ministry of Development staff (24 - 25 February 2009)
4. Trainer in Linux Software Development (FLOSS training) for Teachers and Ministry of Development staff (June 2007 and March 2008)

Industry, Institute, or Organisation Collaboration

Current Projects
1) Cluster Analysis of Cancer Registry with Ministry of Health (Aug 2014 - present)
2) Semi-supervised hierarchical clustering (Feb 2016 - present)
3) Metaheuristics in semi-supervised Fuzzy c-means (Mar 2015-present)
4) Applying Machine Learning techniques on National Health Survey data (Aug 2017 - present)
5) Applying Machine Learning techniques to study driving behaviours collected from simulation (Aug 2017 - present)

Past Project:
1) 2) Evaluate Effects of Cardiac Rehab Programme with MOH (Jun 2014 - Sept 2017)
2) Semi-supervised Fuzzy c-Means clustering methodologies for Real-World Biomedical Data with University of Nottingham (Oct 2010 - May 2014)


Patterns that are not easily determined (due to high number of parameters and records) can be found using data mining techniques, allowing the prediction of outcomes on new data records. Applied in the biomedical field, this enhances the understanding of the disease and its outcome through characterisation of these patterns based on the parameters.

Data Mining and Machine Learning has been widely applied in the field of medicine, agriculture, education, logistics, ecology, retail and so forth. By learning from data, we can find hidden patterns and create useful models to be used for prediction. This has provided decision-support or additional insights to domain experts.

Scopus Publications

Total Publications: 14

1. Neural computing for walking gait pattern identification based on multi-sensor data fusion of lower limb muscles, Neural Computing and Applications

2. On using genetic algorithm for initialising semi-supervised fuzzy c-means clustering, Advances in Intelligent Systems and Computing

3. Multivariate time series clustering analysis for human balance data, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

4. A comparison of distance metrics in semi-supervised hierarchical clustering methods, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

5. An integrated pattern recognition system for knee flexion analysis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

6. An EMG knowledge-based system for leg strength classification and vertical jump height estimation of female netball players, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


8. Identifying stable breast cancer subgroups using semi-supervised fuzzy c-means on a reduced panel of biomarkers, Proceedings of the International Joint Conference on Neural Networks

9. Investigating distance metric learning in semi-supervised fuzzy c-means clustering, IEEE International Conference on Fuzzy Systems

10. A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-means, Central European Journal of Operations Research

11. Improving semi-supervised fuzzy c-means classification of Breast Cancer data using feature selection, IEEE International Conference on Fuzzy Systems

12. Investigating distance metrics in semi-supervised fuzzy c-means for breast cancer classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

13. A preliminary study on automatic breast cancer data classification using semi-supervised Fuzzy c-Means, International Journal of Biomedical Engineering and Technology

14. A comparison of distance-based semi-supervised fuzzy c-means clustering algorithms, IEEE International Conference on Fuzzy Systems