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.
|Author||Christophe Genolini [cre, aut], Bruno Falissard [ctb], Jean-Baptiste Pingault [ctb]|
|Date of publication||2017-08-08 07:31:57 UTC|
|Maintainer||Christophe Genolini <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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