kml3d: K-Means for Joint Longitudinal Data
An implementation of k-means specifically design
to cluster joint trajectories (longitudinal data on
several variable-trajectories).
Like 'kml', it provides facilities to deal with missing
value, compute several quality criterion (Calinski and Harabatz,
Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical
interface for choosing the 'best' number of clusters. In addition, the 3D graph
representing the mean joint-trajectories of each cluster can be exported through
LaTeX in a 3D dynamic rotating PDF graph.
| Version: |
2.5.0 |
| Depends: |
R (≥ 2.10), methods, clv, rgl, misc3d, longitudinalData, kml |
| Published: |
2024-10-23 |
| DOI: |
10.32614/CRAN.package.kml3d |
| Author: |
Christophe Genolini [cre, aut],
Bruno Falissard [ctb],
Patrice Kiener [ctb],
Jean-Baptiste Pingault [ctb] |
| Maintainer: |
Christophe Genolini <christophe.genolini at free.fr> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Citation: |
kml3d citation info |
| Materials: |
NEWS |
| CRAN checks: |
kml3d results |
Documentation:
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