Clustering All‐Cause Mortality Data: A Review

Summary Mortality data are relevant to demography, public health and actuarial science; such data are high‐dimensional, making broad patterns difficult to identify. Clustering is increasingly used to reveal such patterns, yet a review of its application to all‐cause mortality with a focus on country‐level data is lacking. This review therefore summarises recent work and addresses key questions: Why clustering is used, which mortality data are analysed, which clustering methods are most common and what main findings emerge. To address these questions, we examine studies applying clustering to country‐level all‐cause mortality, focusing on mortality indices, data sources and methodological choices, and we replicate some approaches using Human Mortality Database (HMD) data. Our review reveals that clustering is mainly motivated by forecasting and by studying convergence and inequality. Most studies use HMD data from developed countries and rely on k‐means, hierarchical or functional clustering. Main findings include a persistent East–West European division across applications, with clustering generally improving forecast accuracy over single‐country models. Overall, this review highlights the methodological range in the literature, summarises clustering results and identifies gaps, such as the limited evaluation of clustering quality and the underuse of data from countries outside the high‐income world.