K-Means for Joint Longitudinal Data
Description
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.