R help - Jan 2011 - how to calculate the consistency of different clusterings

Dear R-listers,

I do clustering on tens of individuals by thousands of traits. I have
known the assignment of each individual. I want to classify the
individuals by randomly resampling different subsets of the traits,
for example, randomly resampling 100 traits for 100 times, then 200
traits for 100 times, then 300 traits for 100 times, ,,,,,,. By each
subset of traits, I do clustering of the same individuals.

In the end, I want to get the consistency (in percentage) of each of
these clusterings (as examples, here "cluster.1",
"cluster.2" and
"cluster.3" in the dummy data) with the assignment which is already
known ("populations" in the dummy data). I want to know how such work
can be implemented, maybe by using R.

#dummy data,

clus.data <- data.frame(individual = paste("ind", 1:12, sep =
""),
populations = c(rep("popA", 5), rep("popB", 7)), cluster.1 =
c(rep(1,
5), rep(2, 7)), cluster.2 = c(rep(2, 4), rep(1, 8)), cluster.3 c(rep(4, 7),
rep(5, 5)))

clus.data

Thanks.


-- 
Jian-Feng, Mao

the Institute of Botany,
Chinese Academy of Botany,

Hello,

I've been waiting to see if anyone else would answer this.

I've previously used random reallocation of objects to groups
(clusters) as a monte-carlo test of the informativeness of groups, as
described here:

http://lastresortsoftware.blogspot.com/2010/09/monte-carlo-testing-of-classification.html

However, in your case it sounds like you want to investigate the
influence of particular attributes (traits) or groups of attributes on
the classification - is that correct ?  If so, I can probably help
with some R code but I'd need to know the clustering method you are
using (e.g. hclust).

Michael


On 14 January 2011 02:36, Mao Jianfeng <jianfeng.mao at gmail.com>
wrote:
> Dear R-listers,
>
> I do clustering on tens of individuals by thousands of traits. I have
> known the assignment of each individual. I want to classify the
> individuals by randomly resampling different subsets of the traits,
> for example, randomly resampling 100 traits for 100 times, then 200
> traits for 100 times, then 300 traits for 100 times, ,,,,,,. By each
> subset of traits, I do clustering of the same individuals.
>
> In the end, I want to get the consistency (in percentage) of each of
> these clusterings (as examples, here "cluster.1",
"cluster.2" and
> "cluster.3" in the dummy data) with the assignment which is
already
> known ("populations" in the dummy data). I want to know how such
work
> can be implemented, maybe by using R.
>
> #dummy data,
>
> clus.data <- data.frame(individual = paste("ind", 1:12, sep =
""),
> populations = c(rep("popA", 5), rep("popB", 7)),
cluster.1 = c(rep(1,
> 5), rep(2, 7)), cluster.2 = c(rep(2, 4), rep(1, 8)), cluster.3 >
c(rep(4, 7), rep(5, 5)))
>
> clus.data
>
> Thanks.
>
>
> --
> Jian-Feng, Mao
>
> the Institute of Botany,
> Chinese Academy of Botany,
>
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