. You may want to choose a distance
metric that places greater weight on the initial letter.
Peer reviewed research publications, as opposed to idle gossip,
confirm the accuracy of R.
-Original Message-
From: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] On Behalf Of P
You are using the wrong algorithm. You want Partitioning around
Medoids (PAM, function pam), not k-means. PAM is also known as
k-medoids, which is where the confusion may come from.
use
library(cluster)
cl = pam(dis, 4)
and see if you get what you want.
HTH,
Peter
On Mon, Apr 28, 2014 at 9
Cassie,
I am sorry but do you even know what k-means does? That it is a locally
optimal algorithm. That different software implement the same algorithm
differently.
FYI, R uses the Hartigan-Wong (1979) algorithm by default, which is
probably the most efficient out there.
I suggest you first go
Dear R-users,
I am trying to run kmeans on a set comprising of 100 observations. But R
somehow can not figure out the true underlying groups, although other
software such as Jmp, MINITAB are producing the desired result.
Following is a brief example of what I am doing.
library(stringdist)
test=c
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