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......@@ -5,8 +5,10 @@
#'with any number of dimensions that at least contain time_dim.
#'Specifically, it partitions the array along time axis in K groups or clusters
#'in which each space vector/array belongs to (i.e., is a member of) the
#'cluster with the nearest center or centroid. This function relies on the
#'NbClust package (Charrad et al., 2014 JSS).
#'cluster with the nearest center or centroid. This function is a wrapper of
#'kmeans() and relies on the NbClust package (Charrad et al., 2014 JSS) to
#'determine the optimal number of clusters used for K-means clustering if it is
#'not provided by users.
#'
#'@param data A numeric array with named dimensions that at least have
#' 'time_dim' corresponding to time and the dimensions of 'weights'
......@@ -19,9 +21,10 @@
#' 'data'. The default value is 'sdate'.
#'@param nclusters A positive integer K that must be bigger than 1 indicating
#' the number of clusters to be computed, or K initial cluster centers to be
#' used in the method. The default is NULL, and users have to specify which
#' index from NbClust and the associated criteria for selecting the optimal
#' number of clusters will be used for K-means clustering of 'data'.
#' used in the method. The default value is NULL, which means that the number
#' of clusters will be determined by NbClust(). The parameter 'index'
#' therefore needs to be specified for NbClust() to find the optimal number of
#' clusters to be used for K-means clustering calculation.
#'@param index A character string of the validity index from NbClust package
#' that can be used to determine optimal K if K is not specified with
#' 'nclusters'. The default value is 'sdindex' (Halkidi et al. 2001, JIIS).
......
......@@ -28,9 +28,10 @@ dimensions must also be part of the 'data' dimensions.}
\item{nclusters}{A positive integer K that must be bigger than 1 indicating
the number of clusters to be computed, or K initial cluster centers to be
used in the method. The default is NULL, and users have to specify which
index from NbClust and the associated criteria for selecting the optimal
number of clusters will be used for K-means clustering of 'data'.}
used in the method. The default value is NULL, which means that the number
of clusters will be determined by NbClust(). The parameter 'index'
therefore needs to be specified for NbClust() to find the optimal number of
clusters to be used for K-means clustering calculation.}
\item{index}{A character string of the validity index from NbClust package
that can be used to determine optimal K if K is not specified with
......@@ -86,8 +87,10 @@ K-means clustering method using Euclidean distance, of an array of input data
with any number of dimensions that at least contain time_dim.
Specifically, it partitions the array along time axis in K groups or clusters
in which each space vector/array belongs to (i.e., is a member of) the
cluster with the nearest center or centroid. This function relies on the
NbClust package (Charrad et al., 2014 JSS).
cluster with the nearest center or centroid. This function is a wrapper of
kmeans() and relies on the NbClust package (Charrad et al., 2014 JSS) to
determine the optimal number of clusters used for K-means clustering if it is
not provided by users.
}
\examples{
# Generating synthetic data
......
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