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LargeApplicationClustering <-function(Data,ClusterNo,PlotIt=FALSE,Standardization=TRUE,Samples=50,Random=TRUE,...){
# Cls=LargeApplicationClustering(Data,ClusterNo=2)
# Clustering Large Applications (clara)
#
# INPUT
# Data[1:n,1:d] Data set with n observations and d features
# ClusterNo Number of clusters to search for
#
# OPTIONAL
# PlotIt Boolean. Decision to plot or not
# Standardization Data is standardized before calculating the dissimilarities.
# Measurements are standardized for each variable (column), by subtracting the
# variable's mean value and dividing by the variable's mean absolute deviation.
# Samples integer, say N, the number of samples to be drawn from the dataset. Default value
# set as recommended by documentation of cluster::clara.
# Random logical indicating if R's random number generator should be used instead of the primitive clara()-builtin one.
#
# OUTPUT
# Cls[1:n] Clustering of data
# Object Object of cluster::clara algorithm
#
# Author: MT 04/2018
if (!requireNamespace('cluster',quietly = TRUE)) {
message(
'Subordinate clustering package (cluster) is missing. No computations are performed.
Please install the package which is defined in "Suggests".'
)
return(
list(
Cls = rep(1, nrow(Data)),
Object = "Subordinate clustering package (cluster) is missing.
Please install the package which is defined in 'Suggests'."
)
)
}
if(Standardization==1) Standardization=TRUE
if(Standardization==0) Standardization=FALSE
res=cluster::clara(x=Data,k = ClusterNo,samples=Samples,rngR=Random,stand=Standardization,...)
Cls=res$clustering
if(PlotIt){
ClusterPlotMDS(Data,Cls)
}
Cls=ClusterRename(Cls,Data)
return(list(Cls=Cls,Object=res))
}
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