# Clustering of longitudinal data: A tutorial on a variety of approaches

arXiv: https://arxiv.org/abs/2111.05469 · HTML: https://mlsanity.com/paper/2111.05469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 176 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC](https://mlsanity.com/paper/1507.04544/) (2015)
- [Highly comparative feature-based time-series classification](https://mlsanity.com/paper/1401.3531/) (2014)
- [Highly comparative time-series analysis: The empirical structure of time series and their methods](https://mlsanity.com/paper/1304.1209/) (2013)
- [Model-based clustering based on sparse finite Gaussian mixtures](https://mlsanity.com/paper/1606.06828/) (2016)
- [Estimation of extended mixed models using latent classes and latent processes: the R package lcmm](https://mlsanity.com/paper/1503.00890/) (2015)
- [A Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data](https://mlsanity.com/paper/1609.02980/) (2016)
- [Extending Growth Mixture Models Using Continuous Non-Elliptical Distributions](https://mlsanity.com/paper/1703.08723/) (2017)
- [Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models](https://mlsanity.com/paper/1710.10626/) (2017)

## Abstract (arXiv)

During the past two decades, methods for identifying groups with different trends in longitudinal data have become of increasing interest across many areas of research. To support researchers, we summarize the guidance from the literature regarding longitudinal clustering. Moreover, we present a selection of methods for longitudinal clustering, including group-based trajectory modeling (GBTM), growth mixture modeling (GMM), and longitudinal k-means (KML). The methods are introduced at a basic level, and strengths, limitations, and model extensions are listed. Following the recent developments in data collection, attention is given to the applicability of these methods to intensive longitudinal data (ILD). We demonstrate the application of the methods on a synthetic dataset using packages available in R.