Advances a novel adaptation of longitudinal k-means clustering
technique (Genolini et al. (2015) <doi:10.18637/jss.v065.i04>)
for grouping trajectories based on the similarities of their
long-term trends and determines the optimal solution based
on either the average silhouette width (Rousseeuw P. J. 1987)
or the Calinski-Harabatz criterion (Calinski and Harabatz (1974)
<doi:10.1080/03610927408827101>). Includes functions to extract
descriptive statistics and generate a visualisation of the
resulting groups, drawing methods from the 'ggplot2' library (Wickham H. (2016)
<doi:10.1007/978-3-319-24277-4>). The package also includes a number of
other useful functions for exploring and manipulating longitudinal
data prior to the clustering process.

Anchored Kmedoids for Longitudinal Data Clustering

package-ubuntu-20.04-r-4.0.5#cran#akmedoids-1.3.0

A dataframe of alphabetical labels representing
the optimal solution of 'traj' dataset based on 'akClust'
function

A dataframe containing one variable:

<ul><li>
 label: alphabetical label by clusters

</li></ul>

package-ubuntu-20.04-r-4.0.5#cran#akmedoids-1.3.0#docs#clustr

clustr

Description

Usage

Format

akmedoids

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