Understanding the heterogeneity among highly involved gamblers is critical for the development of effective harm reduction strategies. This study employs unsupervised machine learning to segment a population of high-intensity Electronic Gambling Machine (EGM) users based on behavioral indicators derived from transactional data. Using a combination of Uniform Manifold Approximation and Projection (UMAP) for nonlinear dimensionality reduction and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for cluster identification, we performed a systematic grid search to optimize internal validity metrics (Silhouette = 0.5827, Davies–Bouldin Index = 0.4442, Calinski–Harabasz Index = 8907.46). The analysis yielded four well-separated behavioral clusters: one marked by impulsive withdrawals and night-time play; another showing consistent and high-frequency gambling; a third characterized by structured, high-stakes sessions; and a fourth exhibiting rapid, binge-like activity within short time windows. To facilitate interpretation, we trained cluster-wise random forest classifiers, identifying key discriminative features such as balance trajectory, inter-session timing, and variability in transaction intervals. Our findings demonstrate that high involvement is not a uniform construct, but rather encompasses diverse behavioral subtypes, each potentially associated with different levels of gambling-related harm. This segmentation framework offers practical implications for personalized responsible gambling initiatives and contributes to ongoing research advocating for data-driven player protection strategies. • Behavioral segmentation of EGM gamblers using UMAP and DBSCAN. • Grid search optimization yields high internal clustering validity. • Four interpretable clusters reveal heterogeneous gambling patterns. • Random Forest identifies key behavioral features per cluster. • Method supports personalized responsible gambling interventions.
Azizsoltani et al. (Sat,) studied this question.