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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.
Getting started
Browse package contents
Package details |
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| Author | Christophe Genolini [cre, aut], Bruno Falissard [ctb], Patrice Kiener [ctb], Jean-Baptiste Pingault [ctb] |
| Maintainer | Christophe Genolini <christophe.genolini@u-paris10.fr> |
| License | GPL (>= 2) |
| Version | 2.4.6 |
| URL | http:www.r-project.org |
| Package repository | View on CRAN |
| Installation |
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