K-Means for Longitudinal Data

An implementation of k-means specifically design to cluster longitudinal data. 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.


Reference manual

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2.4.1 by Christophe Genolini, 5 years ago


Browse source code at https://github.com/cran/kml

Authors: Christophe Genolini [cre, aut] , Bruno Falissard [ctb]

Documentation:   PDF Manual  

Task views: Cluster Analysis & Finite Mixture Models

GPL (>= 2) license

Depends on methods, clv, longitudinalData

Imported by akmedoids.

Depended on by kml3d, kmlShape.

See at CRAN