kmeans.ct: Continuous-time k-means clustering

Description

A continuous-time version of k-means clustering in which each cluster is a time segments or set of time segments.

kmeans.ct(
  fdobj,
  k,
  common_trend = FALSE,
  init.pts = NULL,
  tol = 0.001,
  max.iter = 100
)

Object of class "kmeans.ct", a list consisting of

fdobj: the supplied fdobj
means: means of the k clusters
transitions: transition points between segments
cluster: cluster memberships in the segments defined by the transitions
size: length of each cluster, i.e. sum of lengths of subintervals making up each cluster
totisd: total integrated sum of distances from the overall mean, analogous to totss from kmeans
withinisd: within-cluster integrated sum of distances, i.e. integrated sum of distances from each cluster mean
tot.withinisd: total within-cluster integrated sum of distances, i.e. sum(withinisd)
betweenisd: between-cluster integrated sum of distances, i.e. totisd-tot.withinss

fdobj: continuous-time multivariate data set of class "fd"
k: number of clusters
common_trend: logical: Should the curves be centered with respect to the mean function? Defaults to FALSE.
init.pts: a set of k time points. The observations at these time points serve as initial values for the k means. Randomly generated if not supplied.
tol: convergence tolerance for the k means
max.iter: maximum number of iterations

Biplab Paul <paul.biplab497@gmail.com> and Philip Tzvi Reiss <reiss@stat.haifa.ac.il>