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Regression modelLongitudinal clustering and latent class growth methods

Criminal Trajectory Clustering

Criminal trajectory clustering is the broad family of methods that group individuals by the shape of their longitudinal offending curves. Rather than committing to a single statistical model, it spans algorithmic approaches — k-means for longitudinal data, distance-based clustering of trajectory shapes, and likelihood-based latent class growth — and treats the choice of clustering method itself as a modeling decision validated by fit and stability criteria.

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Sources

  1. Nagin, D. S. (2005). Group-Based Modeling of Development. Harvard University Press. ISBN: 9780674016866
  2. Genolini, C., & Falissard, B. (2010). KmL: k-means for longitudinal data. Computational Statistics, 25(2), 317–328. DOI: 10.1007/s00180-009-0178-4

How to cite this page

ScholarGate. (2026, June 22). Clustering of Criminal Offending Trajectories. ScholarGate. https://scholargate.app/en/criminology/criminal-trajectory-clustering

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Referenced by

ScholarGateCriminal Trajectory Clustering (Clustering of Criminal Offending Trajectories). Retrieved 2026-06-24 from https://scholargate.app/en/criminology/criminal-trajectory-clustering · Dataset: https://doi.org/10.5281/zenodo.20539026