Clustering longitudinal multivariate trajectories using an ensemble of principal component trees

Clustering longitudinal data is challenging, particularly when measurements are high-dimensional, irregularly sampled, or noisy. We aim to provide a flexible and interpretable framework for identifying meaningful temporal subgroups and their key features. We introduce TAPIO , an ensemble-based clustering approach with longitudinal extensions, including longTAPIO trajectories , longTAPIO sample , and longTAPIO MLD . These variants integrate dimension reduction and cluster-specific feature importance, allowing robust clustering of univariate and multivariate trajectories, as well as regularly and irregularly sampled longitudinal data. Simulation studies demonstrate that TAPIO accurately recovers cluster structure, identifies relevant features, and performs competitively with existing methods. longTAPIO trajectories excels on regularly sampled data, while longTAPIO MLD outperforms alternatives for irregular measurements. Applications on a clinical cohort reveal patient subgroups with distinct survival patterns driven by key cardiac measures, and analyses of high-dimensional longitudinal proteomics data uncover molecularly distinct clusters with interpretable protein-level importance profiles. TAPIO offers a scalable, interpretable framework for longitudinal clustering that accommodates complex multivariate trajectories and high-dimensional data. Its ability to identify both meaningful clusters and their defining features has potential to advance patient stratification, biomarker discovery, and longitudinal data analysis in biomedical research.