[R] custom metric for dist for use with hclust/kmeans
vivek.ayer at gmail.com
Fri May 7 19:17:53 CEST 2010
---------- Forwarded message ----------
From: Vivek Ayer <vivek.ayer at gmail.com>
Date: Fri, May 7, 2010 at 10:17 AM
Subject: Re: [R] custom metric for dist for use with hclust/kmeans
To: Greg Snow <Greg.Snow at imail.org>
The pam function is exactly what I needed. I can now create my own
distance matrix, run as.dist on it and pass it into pam.
Thanks a lot!
On Thu, May 6, 2010 at 12:42 PM, Greg Snow <Greg.Snow at imail.org> wrote:
> The pam function in the cluster package accepts either raw data or a dissimilarity matrix and does the same idea as kmeans. The daisy function has more options for creating the dissimilarity matrix, if what you want is not in there, you could still use it as a model for creating your own function. You could also use the outer function and as.dist to create the distance matrix.
> Gregory (Greg) L. Snow Ph.D.
> Statistical Data Center
> Intermountain Healthcare
> greg.snow at imail.org
>> -----Original Message-----
>> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
>> project.org] On Behalf Of Vivek Ayer
>> Sent: Thursday, May 06, 2010 9:56 AM
>> To: r-help at r-project.org
>> Subject: Re: [R] custom metric for dist for use with hclust/kmeans
>> Bump...no insights on defining custom metrics. Guess I'll give the
>> other languages a shot.
>> On Wed, May 5, 2010 at 10:13 AM, Vivek Ayer <vivek.ayer at gmail.com>
>> > Hi guys,
>> > I've been using the kmeans and hclust functions for some time now and
>> > was wondering if I could specify a custom metric when passing my data
>> > frame into hclust as a distance matrix. Actually, kmeans doesn't even
>> > take a distance matrix; it takes the data frame directly. I was
>> > wondering if there's a way or if there's a package that lets you
>> > create distance matrices from non-standard metrics, e.g.,
>> > KL-divergence (which is not really one), but metrics used often in
>> > information theory. I'm assuming kmeans just assumes the euclidean
>> > metric, but it would be nice to customize that as well. Is stuff out
>> > there, or would I have create my own?
>> > Thanks,
>> > Vivek
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