traj (version 3.0.0)

trajClusters: Classify the Longitudinal Data Based on the Measures.

Description

Classifies the trajectories by applying a nonparametric clustering algorithm to the measures computed by trajMeasures().

Usage

trajClusters(
  Measures,
  select = NULL,
  fuzzy = FALSE,
  nclusters = NULL,
  nstart = 50
)
# S3 method for trajClusters
print(x, ...)
# S3 method for trajClusters
summary(object, ...)

Value

An object of class trajClusters; a list containing the result of the clustering, as well as a curated form of the arguments. If nclusters is set to NULL, clustering is carried out for each number \(k\) of clusters between 2 and (up to) 8 and a plot is produced representing the value of three internal cluster validity indices (C-index, Calinski-Harabasz, Wemmert-Gancarski) as a function of \(k\). As in the 'KmL' package of Genolini et al., these validity indices are presented on a scale from 0 to 1, with 1 corresponding to the highest validity score and 0 corresponding to the lowest. From this, a "best" value of \(k\) is determined using a ranked voting system.

Arguments

Measures

object of class trajMeasures as returned by the function trajMeasures().

select

an optional vector of positive integers corresponding to the measures to use in the clustering. Defaults to NULL, which uses all the measures contained in Measures.

fuzzy

logical. If FALSE, each trajectory is assigned to a unique group. If TRUE, each trajectory is assigned a "degree of membership" to each group. Defaults to FALSE.

nclusters

The desired number of clusters. If NULL, clustering is carried out for every number of clusters between 2 and (up to) 8 and the "best" number of clusters is used, as judged by the combination of three internal cluster validity indices. See section 'Value' for more details. Defaults to NULL.

nstart

The number of random starts. Defaults to 50.

x

object of class trajClusters.

...

further arguments passed to or from other methods.

object

object of class trajClusters.

Details

The spectral clustering algorithm presented in Meila (2005) is implemented in which the similarity matrix \(S\) is built from a binary K nearest neighbors similarity function (\(S=(W+W^T)/2\), where \(W_{ij}=1\) if data point \(j\) is among the nearest points to data point \(i\) and \(W_{ij}=0\) otherwise).

References

Genolini, C. et al., kml: K-Means for Longitudinal Data, https://CRAN.R-project.org/package=kml

Meila, M., Spectral Clustering. Handbook of Cluster Analysis, Chapter 7, Chapman and Hall/CRC, 2005.

Examples

if (FALSE) {
data("trajdata")
trajdata.noGrp <- trajdata[, -which(colnames(trajdata) == "Group")] # remove the Group column

m = trajMeasures(trajdata.noGrp, ID = TRUE, measures = 1:19)

s2.3 <- trajClusters(m, nclusters = 3)
plot(s2.3)

#'s2.4 <- trajClusters(m, nclusters = 4)
plot(s2.4)

#'s2.5 <- trajClusters(m, nclusters = 5)
plot(s2.5)

groups <- s2.4 <- trajClusters(m, nclusters = 4)$partition
}