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ADPclustering=function(Data,ClusterNo=NULL,PlotIt=FALSE,...){
# 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
#
# OUTPUT
# Cls[1:n] Clustering of data
# Object Object of adpclust
#
# Author: MT, 04/2018
if (!requireNamespace('ADPclust', quietly = TRUE)) {
message(
'Subordinate clustering package (ADPclust) 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 (ADPclust) is missing.
Please install the package which is defined in 'Suggests'."
)
)
}
if (!requireNamespace('cluster', quietly = TRUE)) {
message(
'Subordinate clustering package (cluster) is missing although its imported in ADPclust. 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 although its imported in ADPclust.
Please install the package which is defined in 'Suggests'."
)
)
}
if(is.null(ClusterNo))
adp=ADPclust::adpclust(Data,...)
else
adp=ADPclust::adpclust(Data,nclust=ClusterNo,...)
Cls=as.numeric(adp$clusters)
Cls=ClusterRename(Cls,Data)
if(PlotIt){
ClusterPlotMDS(Data,Cls)
}
return(list(Cls=Cls,Object=adp))
}
#other package, were params have to be chosen
# DensityPeakClustering=function(DataOrDistances,Knn=10,rho=2, delta=2, method="euclidean",PlotIt=TRUE,...){
# #Rodriguez, A., & Laio, A.: Clustering by fast search and find of density peaks. Science, 344(6191), 1492-1496. doi:10.1126/science.1242072, 2014.
# requireNamespace('densityClust')
# if(!is.matrix(DataOrDistances)){
# warning('DataOrDistances is not a matrix. Calling as.matrix()')
# DataOrDistances=as.matrix(DataOrDistances)
# }
# if(!mode(DataOrDistances)=='numeric'){
# warning('Data is not a numeric matrix. Calling mode(DataOrDistances)="numeric"')
# mode(DataOrDistances)='numeric'
# }
# AnzData = nrow(DataOrDistances)
#
# if (!isSymmetric(DataOrDistances)) {
# requireNamespace('parallelDist')
#
# Distances=as.matrix(parallelDist::parDist(DataOrDistances,method=method))
# }
#
# out=densityClust::densityClust(Distances,...)
#
# if(PlotIt){
# requireNamespace('DataVisualizations')
# if (!isSymmetric(DataOrDistances)) {
# DataVisualizations::Plot3D(DataOrDistances,Cls,k=Knn)
# }else{
# requireNamespace('ProjectionBasedClustering')
#
# DataVisualizations::Plot3D(DataOrDistances,ProjectionBasedClustering::MDS(DataOrDistances,OutputDimension = 3)$ProjectedPoints)
# }
# }
# return(list(Cls=NULL,DPobject=out))
# }
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