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In social and behavioural sciences, longitudinal clustering is widely used for identifying groups of individual trends that correspond to certain developmental processes over time. Whilst popular clustering techniques, such as k-means, are suited for identifying spherical clusters (Curman et al., 2015; Genolini & Falissard, 2011), there has been little attempt to modify such methods to identify alternative forms of cluster, such as those that represent linear growth over time (i.e. directionally-homogeneous clusters). To address this shortcoming, we introduce Anchored k-medoids, a package referred to as Ak-medoids, which implements a medoid-based expectation maximisation (MEM) procedure within a classical k-means clustering framework. The package includes functions to assist in the manipulation of longitudinal data sets prior to the clustering procedure, and the visualisation of solutions post-procedure. The potential application areas of Ak-medoids include criminology, transport, epidemiology and brain imaging.

Akmedoids R package for generating directionally-homogeneous clusters of longitudinal data sets