I'm not going to comment on column names, but this is just to make you
aware that the results of k-means depend on random initialisation.
This means that it is possible that you get different results if you run
it several times. It basically gives you a local optimum and there may be
more than one of these.
Use set.seed to see whether this explains your problem.
Best regards,
Christian
On Wed, 6 Apr 2011, Raji wrote:
> Hi All,
>
> I was using the following command for performing kmeans for Iris dataset.
>
> Kmeans_model<-kmeans(dataFrame[,c(1,2,3,4)],centers=3)
>
> This was giving proper results for me. But, in my application we generate
> the R commands dynamically and there was a requirement that the column
names
> will be sent instead of column indices to the R commands.Hence, to
> incorporate this, i tried using the R commands in the following way.
>
>
kmeans_model<-kmeans((SepalLength+SepalWidth+PetalLength+PetalWidth),centers=3)
>
> or
>
>
kmeans_model<-kmeans(as.matrix(SepalLength,SepalWidth,PetalLength,PetalWidth),centers=3)
>
> In both the ways, we found that the results are different from what we saw
> with the first command (with column indices).
>
> can you please let us know what is going wrong here.If so, can you please
> let us know how the column names can be used in kmeans to obtain the
correct
> results?
>
> Many thanks,
> Raji
>
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>
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*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche