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AgglomerativeNestingClustering <-function(DataOrDistances,ClusterNo,PlotIt=FALSE,Standardization=TRUE,...){
# Cls=DivisiveAnalysisClustering(Data,ClusterNo=2)$Cls
# DivisiveAnalysisClustering (Diana)
# Returns class assignment
#
# INPUT
# DataOrDistances[1:n,1:d] Dataset with n observations and d features or distance matrix with size n
# ClusterNo Number of clusters to search for
#
# OPTIONAL
# PlotIt Boolean. Decision to plot or not.
# Standardization Boolean. If TRUE, then data gets standardized before calculating dissimilarities.
# If distances are given, this argument gets ignored
#
# OUTPUT
# Cls[1:n] Clustering of data
# Object Object of sota algorithm
# Dendrogram
#
# Author: MT 04/2018
# if(missing(DataOrDistances)){
# DataOrDistances=Data
# }
if(Standardization==1) Standardization=TRUE
if(Standardization==0) Standardization=FALSE
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(DataOrDistances)),
Object = "Subordinate clustering package (cluster) is missing.
Please install the package which is defined in 'Suggests'."
)
)
}
if (isSymmetric(unname(DataOrDistances))) {
Input = as.dist(DataOrDistances)
requireNamespace('ProjectionBasedClustering')
AnzVar = ncol(DataOrDistances)
AnzData = nrow(DataOrDistances)
diss =TRUE
}else{
Input=DataOrDistances
diss =FALSE
}
res=cluster::agnes(x=Input,diss =diss,stand=Standardization,...)
if(length(ClusterNo)!=1){
stop('ClusterNo has to be a numerical number not a vector of length higher than 1 or another object.')
}
if(ClusterNo>0){
Cls=cutree(as.hclust(res), k = ClusterNo)
if(PlotIt){
ClusterPlotMDS(DataOrDistances,Cls)
}
Cls=ClusterRename(Cls,DataOrDistances)
}
if(ClusterNo<=0){
Cls=NULL
plot(res)
if(ClusterNo<0){
warning(('ClusterNo cannot be a negativ number'))
}
}
return(list(Cls=Cls, Object=res, Dendrogram=as.dendrogram(as.hclust(res))))
}
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