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HierarchicalClustering=function(DataOrDistances,ClusterNo,Type='SingleL',Fast=TRUE,Data,...){
# INPUT
# Data[1:n,1:d] Data set with n observations and d features or distance matrix of size n
# ClusterNo Number of clusters to search for
#
# OPTIONAL
# Type Type of cluster analysis: "Ward", "SingleL", "CompleteL", "AverageL" (UPGMA),
# "WPGMA" (mcquitty), "MedianL" (WPGMC), "CentroidL" (UPGMC), "Minimax", "MinEnergy",
# "Gini" or "HDBSCAN".
# Fast Boolean. If TRUE and fastcluster installed, then a faster implementation of the Types
# above can be used except for "Minimax", "MinEnergy", "Gini" or "HDBSCAN"
#
# OUTPUT
# Cls[1:n] Clustering of data
# Object Object of adpclust
#
# Author: MT, 04/2018
if(missing(DataOrDistances)){
DataOrDistances=Data
}
if(missing(ClusterNo)) ClusterNo=0
# Unification for paper
if(Type=='SingleL') Type="single"
if(Type=="Ward") Type="ward.D2"
if(Type=='CompleteL') Type="complete"
if(Type=='AverageL') Type="average"
if(Type=='WPGMA') Type="mcquitty"
if(Type=='MedianL') Type="median"
if(Type=='CentroidL') Type="centroid"
# Backwards compatibility to matlab, otherwise could be programmed better :-(
if(Type=='MinEnergy'){
return(MinimalEnergyClustering(DataOrDistances = DataOrDistances,ClusterNo = ClusterNo,...))
}else if(Type=="Gini"){
return(GenieClustering(DataOrDistances = DataOrDistances,ClusterNo = ClusterNo,...))
}else if(Type=="Minimax"){
return(MinimaxLinkageClustering(DataOrDistances = DataOrDistances,ClusterNo = ClusterNo,...))
}else if(Type=="Sparse"){
return(SparseClustering(DataOrDistances = DataOrDistances,ClusterNo = ClusterNo,Strategy = "Hierarchical",...))
}else if(Type=="HDBSCAN"){
V=HierarchicalDBSCAN(DataOrDistances = DataOrDistances,...)
if(ClusterNo>1){
Cls = cutree(V$Tree, ClusterNo)
}else{
#ClusterDendrogram(V$Tree,1,Colorsequence = 'black',main = 'HDBSCAN Clustering')
Cls=V$Cls#automatic number of clusters selection by Hierarchical_DBSCAN
}
return(list(Cls=Cls,Dendrogram=V$Dendrogram,Object=V$Tree,OriginalObject=V$Object))
}else if (isSymmetric(unname(DataOrDistances))) {
if(!inherits(DataOrDistances,'dist')){
Input=as.dist(DataOrDistances)
}else{
Input=DataOrDistances
}
return(HierarchicalClusterDists(pDist = Input,ClusterNo = ClusterNo,Type = Type,Fast=Fast,...))
}else{# Data given
return(HierarchicalClusterData(Data = DataOrDistances,ClusterNo = ClusterNo,Type = Type,Fast=Fast,...))
}#endisSymmetric(DataOrDistances)
}
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