Clustering Longitudinal Data: A Review of Methods and Software Packages

Summary Clustering of longitudinal data is becoming increasingly popular in many fields such as social sciences, business, environmental science, medicine and healthcare. However, it is often challenging due to the complex nature of the data, such as dependencies between observations collected over time, missingness, sparsity and non‐linearity, making it difficult to identify meaningful patterns and relationships among the data. Despite the increasingly common application of cluster analysis for longitudinal data, many existing methods are still less known to researchers, and limited guidance is provided in choosing between methods and software packages. In this paper, we review several commonly used methods for clustering longitudinal data. These methods are broadly classified into three categories, namely, model‐based approaches, algorithm‐based approaches and functional clustering approaches. We perform a comparison among these methods and their corresponding R software packages using real‐life datasets and simulated datasets under various conditions. Findings from the analyses and recommendations for using these approaches in practice are discussed.