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ModelBasedClustering <-function(Data,ClusterNo=2,PlotIt=FALSE,...){
# Cls <- MoGclustering(Data,ClusterNo);
# call R's Model based clustering or MixtureOfGaussians (MoG) clustering
#
# 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 mclust::Mclust algorithm
#
# MT 2017
# Uebersicht/Kurz-Zfssg in [Thrun, 2017, p. 23]
#
# [Thrun, 2017] Thrun, M. C.:A System for Projection Based Clustering through Self-Organization and Swarm Intelligence, (Doctoral dissertation), Philipps-Universitaet Marburg, Marburg, 2017.
# Algorithmus from:
# [Fraley/Raftery, 2002] Fraley, C., & Raftery, A. E.: Model-based clustering, discriminant analysis, and density estimation, Journal of the American Statistical Association, Vol. 97(458), pp. 611-631. 2002.
# [Fraley/Raftery, 2006] Fraley, C., & Raftery, A. E.MCLUST version 3: an R package for normal mixture modeling and model-based clustering,DTIC Document, 2006.
if (!requireNamespace('mclust',quietly = TRUE)) {
message(
'Subordinate clustering package (mclust) 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 (mclust) is missing.
Please install the package which is defined in 'Suggests'."
)
)
}
if (ClusterNo<2){
warning("ClusterNo should be an integer > 2. Now, all of your data is in one cluster.")
if(is.null(nrow(Data))){# dann haben wir einen Vektor
return(cls <- rep(1,length(Data)))
}else{ # Matrix
return(cls <- rep(1, nrow(Data)))
}
}
res=mclust::Mclust(Data,G=ClusterNo,modelNames=mclust::mclust.options("emModelNames"),...)
Cls=res$classification
if(PlotIt){
ClusterPlotMDS(Data,Cls)
}
Cls=ClusterRename(Cls,Data)
return(list(Cls=Cls,Object=res))
}
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