latrend-approaches: High-level approaches to longitudinal clustering
In latrend: A Framework for Clustering Longitudinal Data

latrend-approaches

R Documentation

High-level approaches to longitudinal clustering

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:

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denteuling2021clusteringlatrend: A tutorial and overview on methods for longitudinal clustering.
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denteuling2021comparison;textuallatrend compared KmL, MixTVEM, GBTM, GMM, and GCKM.
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twisk2012classifying;textuallatrend compared KmL, GCKM, LLCA, GBTM and GMM.
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verboon2022clustering;textuallatrend compared the kml, traj and lcmm packages in R.
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martin2015growth;textuallatrend compared KmL, LCA, and GMM.

Disclaimer: The table below has been adapted from a pre-print of \insertCitedenteuling2021clusteringlatrend.

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.

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