Hi the list
I try the flexclust, but I do not manage to see what is wrong in my
(very simple) code...
Will you have few minutes to check it?
Thanks for your help.
Christophe
--- 8< --------------------------------
data <- rbind(c(1,2 ,NA,4 ),
c(1,1 ,NA,1 ),
c(2,3 ,4 ,5 ),
c(2,2 ,2 ,2 ),
c(3,NA,NA,6 ),
c(3,NA,NA,3 ),
c(2,4 ,4 ,NA),
c(2,3 ,2 ,NA))
distTest <- rbind(c(0,0,0,0),
c(1,1,1,1))
distNA <- function(x,centers){
z <- matrix(0,nrow=nrow(x),ncol=nrow(centers))
for(k in 1:nrow(centers)){
z[,k]<- apply(x,1,function(x){dist(rbind(x,centers[k,]))})
}
z
}
distNA(data,distTest)
km <- kccaFamily(dist=distNA,cent=colMeans)
kcca(x=data,k=2,family=km)
kcca(x=data,k=3,family=km)
--- 8< --------------------------------
On Mon, 12 May 2008 19:24:55 +0200,
cgenolin (c) wrote:
> Hi the devel list,
> I am using K means with a non standard distance. As far as I see, the
> function kmeans is able to deal with 4 differents algorithm, but not
> with a user define distance.
> In addition, kmeans is not able to deal with missing value whereas
> there is several solution that k-means can use to deal with them ; one
> is using a distance that takes the missing value in account, like a
> distance with Gower adjustement (which is the regular distance dist()
> used in R).
> So is it possible to adapt kmeans to let the user gives an argument
> 'distance to use'?
As Bill Venables already pointed out that makes not too much sense,
especially as there are already R functions for doing that. Package
flexclust implements a k-means-type clustering algorithm where the
user can provide arbitrary distance measures, have a look at
http://www.stat.uni-muenchen.de/~leisch/papers/Leisch-2006.pdf
The code you need to write for using a new distance measure is
minimal, and there are two examples in the paper describing in detail
what needs to be done.
Hope this helps,
Fritz Leisch
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