Anchored Kmedoids for Longitudinal Data Clustering

Advances a novel adaptation of longitudinal k-means clustering technique (Genolini et al. (2015) ) 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) ). Includes functions to extract descriptive statistics and generate a visualisation of the resulting groups, drawing methods from the 'ggplot2' library (Wickham H. (2016) ). The package also includes a number of other useful functions for exploring and manipulating longitudinal data prior to the clustering process.

akmedoids

An R package for analyzing and clustering longitudinal data

Description

The akmedoids package advances the clustering of longitudinal datasets in order to identify clusters of trajectories with similar long-term linear trends over time, providing an improved cluster identification as compared with the classic kmeans algorithm. The package also includes a set of functions for addressing common data issues, such as missing entries and outliers, prior to conducting advance longitudinal data analysis. One of the key objectives of this package is to facilitate easy replication of a recent paper which examined small area inequality in the crime drop (Adepeju et al.2021). Many of the functions provided in the akmedoids package may be applied to longitudinal data in general.

For more information and usability, check out details on CRAN.

Support and Contributions:

For support and bug reports send an email to: [email protected] or open an issue here. Code contributions to akmedoids are also very welcome.

References:

Adepeju, M., Langton, S. and Bannister, J. (2021). Anchored k-medoids: a novel adaptation of k-medoids further refined to measure instability in the exposure to crime. Journal of Computational Social Science link