[EMAIL PROTECTED] wrote:
n.bouget wrote:
Hi,
I want to know which distance is using in the function kmeans
and if we can change this distance. Indeed, in the function pam, we can put a distance matrix in
parameter (by the line "pam<-pam(dist(matrixdata),k=7)" ) but
we can't do it in the function kmeans, we have to put the
matrix of data directly ...
Yes but how can we choose the distance to calculate centers?
Ah, you are going to use different distance measure (e.g. euclidean, manhattan, ...) as in other cluster methods? Well, that's not possible with the kmeans() implementation. See ?kmeans which tells you:
The data given by x is clustered by the k-means algorithm. When this terminates, all cluster centres are at the mean of their Voronoi sets (the set of data points which are nearest to the cluster centre).
The algorithm of Hartigan and Wong (1979) is used.
Of course, you can do some projection based on the calculation of distances, but I don't think there are functions available to do that completely automatical - and interpretation of results won't be that easy ...
Uwe Ligges
Thanks in advance, Nicolas BOUGET
As the name says, kmeans() calculates *means* (centres) of
clusters. It
does not any make sense to do that on distances ...
Uwe Ligges
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