This page provides high-level guidelines on which methods are applicable to your dataset. Note that this is intended as a quick-start.
Recommended overview and comparison papers:
denteuling2021clusteringlatrend: A tutorial and overview on methods for longitudinal clustering.
denteuling2021comparison;textuallatrend compared KmL, MixTVEM, GBTM, GMM, and GCKM.
twisk2012classifying;textuallatrend compared KmL, GCKM, LLCA, GBTM and GMM.
verboon2022clustering;textuallatrend compared the kml, traj and lcmm packages in R.
martin2015growth;textuallatrend compared KmL, LCA, and GMM.
Disclaimer: The table below has been adapted from a pre-print of denteuling2021clusteringlatrend.
| Approach | Strengths | Limitations | Methods |
| Cross-sectional clustering | Suitable for large datasets — Many available algorithms — Non-parametric cluster trajectory representation | Requires time-aligned complete data — Sensitive to measurement noise | lcMethodKML lcMethodMclustLLPA lcMethodMixtoolsNPRM |
| Distance-based clustering | Suitable for medium-sized datasets — Many distance metrics — Distance matrix only needs to be computed once | Scales poorly with number of trajectories — No robust cluster trajectory representation — Some distance metrics require aligned observations | lcMethodDtwclust |
| Feature-based clustering | Suitable for large datasets — Configurable — Features only needs to be computed once — Compact trajectory representation | Generally requires intensive longitudinal data — Sensitive to outliers | lcMethodFeature lcMethodAkmedoids lcMethodLMKM lcMethodGCKM |
| Model-based clustering | Parametric cluster trajectory — Incorporate (domain) assumptions — Low sample size requirements | Computationally intensive — Scales poorly with number of clusters — Convergence challenges | lcMethodLcmmGBTM lcMethodLcmmGMM lcMethodCrimCV lcMethodFlexmix lcMethodFlexmixGBTM lcMethodFunFEM lcMethodMixAK_GLMM lcMethodMixtoolsGMM lcMethodMixTVEM |
It is strongly encouraged to evaluate and compare several candidate methods in order to identify the most suitable method.