Ensemble clustering: A practical tutorial
Abstract
Cluster analysis is an explorative analytical method, serving as a critical tool in psychology, psychiatry and related fields to map heterogeneous data into meaningful subgroups. Despite their extensive historical use, traditional clustering techniques suffer from a lack of stability, robustness, and generalisability. These issues stem from the inherent difficulties of the clustering optimization problem as well as the stochastic nature of algorithm optimizers. To address these challenges, we demonstrate the use of methods utilising ensemble learning techniques to combine clustering results from different algorithms, model specifications, and/or sampled sub-datasets to form a single, more reliable consensus of clustering solutions. We detail ensemble clustering principles, variations in base clustering generation models, and consensus methods. Detailed introductions in existing R libraries and practical examples using R code are provided to guide users in both implementing and optimising ensemble clustering models. We then include simulation studies of real-world data to demonstrate the substantial benefit of ensemble clustering compared with single-run clustering models. The resources presented here will enable researchers to apply advanced clustering techniques to decompose heterogeneous and complex psychological data into stable subgroups.
Related
- Bayesian Hierarchical Models for Longitudinal Meta-Analysis in Sports Science: A Practical Tutorial Using brms
- Response to comment on “Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper”
- Why and How to Model Trends and Time-Varying Experimental Effects Using Mixed-Effects Models: A Simulation Study and a Tutorial
- Choosing Stimuli Wisely by Using Individual Differences: A Step-by-Step Tutorial for the One-Stimulus-Per-Factor Principle in Vision Science and Optometric Assessment
- Indirect Calorimetry in Oncology: A Case-Based Clinical Tutorial
BibTeX
@misc{gao2026ensemble,
title = {{Ensemble clustering: A practical tutorial}},
author = {Caroline X. Gao and Shengqi Wang and Ye Zhu and Myriam Ziou and Shu Mei Teo and Catherine L Smith and Derek Chiu and Aline Talhouk and Sue M. Cotton and Dominic Dwyer},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/fq6e9_v2},
url = {https://osf.io/preprints/psyarxiv/fq6e9_v2/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Ensemble clustering: A practical tutorial
AU - Caroline X. Gao
AU - Shengqi Wang
AU - Ye Zhu
AU - Myriam Ziou
AU - Shu Mei Teo
AU - Catherine L Smith
AU - Derek Chiu
AU - Aline Talhouk
AU - Sue M. Cotton
AU - Dominic Dwyer
PY - 2026
DA - 2026/09/01
PB - PsyArXiv
DO - 10.31234/osf.io/fq6e9_v2
UR - https://osf.io/preprints/psyarxiv/fq6e9_v2/
AB - Cluster analysis is an explorative analytical method, serving as a critical tool in psychology, psychiatry and related fields to map heterogeneous data into meaningful subgroups. Despite their extensive historical use, traditional clustering techniques suffer from a lack of stability, robustness, and generalisability. These issues stem from the inherent difficulties of the clustering optimization problem as well as the stochastic nature of algorithm optimizers. To address these challenges, we demonstrate the use of methods utilising ensemble learning techniques to combine clustering results from different algorithms, model specifications, and/or sampled sub-datasets to form a single, more reliable consensus of clustering solutions. We detail ensemble clustering principles, variations in base clustering generation models, and consensus methods. Detailed introductions in existing R libraries and practical examples using R code are provided to guide users in both implementing and optimising ensemble clustering models. We then include simulation studies of real-world data to demonstrate the substantial benefit of ensemble clustering compared with single-run clustering models. The resources presented here will enable researchers to apply advanced clustering techniques to decompose heterogeneous and complex psychological data into stable subgroups.
ER -