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Clustering of Longitudinal Data: A Tutorial on a Variety of Approaches

Niek Den Teuling et al.

Statistical Analysis and Data Mining2026https://doi.org/10.1002/sam.70052preprint
AJG 1
Weight
0.41

What the paper says

During the past two decades, methods for identifying groups with different trends in longitudinal data involving a single numeric outcome have become of increasing interest across many areas of research. To support researchers, we summarize the guidance from literature regarding the clustering of such data. Moreover, we present a selection of methods that take different approaches to clustering longitudinal data, based on cross‐sectional clustering, distance‐based clustering, feature‐based clustering, and mixture modeling. 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.

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https://doi.org/https://doi.org/10.1002/sam.70052

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@article{niek2026,
  title        = {{Clustering of Longitudinal Data: A Tutorial on a Variety of Approaches}},
  author       = {Niek Den Teuling et al.},
  journal      = {Statistical Analysis and Data Mining},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/sam.70052},
}

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Clustering of Longitudinal Data: A Tutorial on a Variety of Approaches

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.