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.

Package details

AuthorChristophe Genolini [cre, aut], Bruno Falissard [ctb], Jean-Baptiste Pingault [ctb]
MaintainerChristophe Genolini <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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kml3d documentation built on May 2, 2019, 10:51 a.m.