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.