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SharedNearestNeighborClustering <-function(Data,Knn,Radius,minPts,PlotIt=FALSE,UpperLimitRadius,...){
# Cls=SharedNearestNeighborClustering(FCPS$Hepta$Data,sqrt(min(res$withinss)))
# DBscan nach [Ester et al., 1996]
#
# INPUT
# Data[1:n,1:d] Data set with n observations and d features
# Radius eps, radius of R-ball [Ester et al., 1996, p. 227],size of the epsilon neighborhood.
#
# OPTIONAL
# Knn Number of neighbors to consider to calculate the shared nearest neighbors.
# Radius eps [Ester et al., 1996, p. 227] neighborhood in the R-ball graph/unit disk graph),
# size of the epsilon neighborhood. If NULL, automatic estimation is done using
# insights of [Ultsch, 2005].
# minPts In principle minimum number of points in the unit disk, if the unit disk is within the cluster (core) [Ester et al., 1996, p. 228].
# number of minimum points in the eps region (for core points). Default is 5 points.
# PlotIt Boolean. Decision to plot or not
# UpperLimitRadius Limit for radius search, experimental
#
# OUTPUT
# Cls[1:n] Clustering of data. Points which cannot be assigned to a cluster will be reported as members of the noise cluster with NaN.
# Object Object defined by clustering algorithm as the other output of this algorithm
#
# Author: MT 2019
if (!requireNamespace('dbscan',quietly = TRUE)) {
message(
'Subordinate clustering package (dbscan) 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 (dbscan) is missing.
Please install the package which is defined in 'Suggests'."
)
)
}
if(is.null(nrow(Data))){# then we get a vector
return(cls <- rep(1,length(Data)))
}
if(is.null(Radius)){
if(requireNamespace("DataVisualizations",quietly = TRUE)){
warning('The Radius (eps) parameter is missing but it is required in DBscan. Trying to estimate..')
Radius=0.5*DataVisualizations::ParetoRadius(Data)
}
else{
stop('DataVisualizations package not loaded or installed.')
}
}
if(is.null(minPts)){
minPts=min(round(0.0005*nrow(Data),2),20)## A point needs at least 16 (minPts) links in the sNN graph to be a core point.
warning('The minPts parameter is missing but it is required in DBscan. Trying to estimate..')
}
if(missing(UpperLimitRadius))
UpperLimitRadius=2*Radius
liste=dbscan::sNNclust(x = Data,k=Knn,eps=Radius,minPts=minPts,...)
Cls=liste$cluster
ind=which(Cls==0)
Cls[!is.finite(Cls)]=0
# Noise points have cluster id 0
if(Radius<UpperLimitRadius&sum(Cls==0)>round(0.025*nrow(Data))){
out=suppressWarnings(SharedNearestNeighborClustering(Data,Knn=Knn,Radius=Radius*1.01,minPts=minPts,PlotIt=PlotIt,UpperLimitRadius=UpperLimitRadius,...))
Cls=out$Cls
liste=out$SNNobject
}
if(!is.null(rownames(Data))){
names(Cls)=rownames(Data)
}
if(PlotIt){
Cls2=Cls
Cls2[Cls2==0]=999
ClusterPlotMDS(Data,Cls2)
}
Cls=ClusterRename(Cls,Data)
return(list(Cls=Cls,Object=liste))
}
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