ScholarGate
Asistent
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.

Otvorite u MethodMindUskoroPrimijenite, usporedite, dobijte smjernice
Alati i resursi
Preuzmi prezentaciju
Učenje i istraživanje
VideoUskoro

Pročitajte cijelu metodu

Samo za članove

Prijavite se besplatnim računom kako biste pročitali ovaj odjeljak.

Prijavite se

Karta metoda

Okruženje srodnih metoda — odaberite čvor za istraživanje.

Izvori

  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

Kako citirati ovu stranicu

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

Koja metoda?

Postavite ovu metodu uz njoj najsrodnije i pročitajte ih jednu uz drugu — knjižnica vam knjige stavlja na stol; izbor je na vama.

Usporedi jedno uz drugo

Citirana u

ScholarGateCriminal Trajectory Clustering (Clustering of Criminal Offending Trajectories). Preuzeto 2026-06-25 s https://scholargate.app/hr/criminology/criminal-trajectory-clustering · Skup podataka: https://doi.org/10.5281/zenodo.20539026