I still don't quite understand. I thought kmeans algorithm went something like
this:
Iterate until stable :
Determine the centroid coordinate
Determine the distance of each object to the centroids
Group the object based on minimum distance
So, why do I not want a distance matrix?
Christian Hennig <[EMAIL PROTECTED]> wrote: On Mon, 7 Aug 2006, Ffenics wrote:
> well then i dont understand because everything i have read so far suggests
> that you use the dist() function to create a matrix based on the euclideam
> distance and then the kmeans() function.
kmeans requires a data matrix where cases are rows and variables are
columns. (If you understand what kmeans does, you should know why - means
can't be computed from distances.)
I'm not sure about the NA behaviour. I guess NAs produce an error? (Try it
ou!)
Anyway, I'd think about casewise deletion or imputation if I had to run
kmeans on data with missing values.
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