Dear Alex,
actually fixing the number of clusters in kmeans end then ending up with a
smaller number because of empty clusters is not a standard method of
estimating the number of clusters. I may happen (as apparently in some of
your examples), but it is generally rather unusual. In most cases
I use kmeans to classify spectral events in high and low 1/3 octave bands:
#Do cluster analysis
CyclA<-data.frame(LlowA,LhghA)
CntrA<-matrix(c(0.9,0.8,0.8,0.75,0.65,0.65), nrow = 3, ncol=2, byrow=TRUE)
ClstA<-kmeans(CyclA,centers=CntrA,nstart=50,algorithm="MacQueen")
This works well when the actu
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