Dear helpers I was working with kmeans from package mva and found some strange situations. When I run several times the kmeans algorithm with the same dataset I get the same partition. I simulated a little example with 6 observations and run kmeans giving the centers and making just one iteration. I expected that the algorithm just allocated the observations to the nearest center but think this is not the result that I get... Here are the simulated data > dados<-matrix(c(-1,0,2,2.5,7,9,0,3,0,6,1,4),6,2) > dados [,1] [,2] [1,] -1.0 0 [2,] 0.0 3 [3,] 2.0 0 [4,] 2.5 6 [5,] 7.0 1 [6,] 9.0 4 > plot(dados) > dados<-matrix(c(-1,0,2,2.5,7,9,0,5,0,6,1,4),6,2) > plot(dados) > A<-kmeans(dados,dados[c(3,4),],1) > A $cluster [1] 1 1 1 1 2 2 $centers [,1] [,2] 1 0.875 2.75 2 8.000 2.50 $withinss [1] 38.9375 6.5000 $size [1] 4 2 Any hints? Thanks a lot Luis Silva
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