R help - Oct 2007 - pseudo code

Hey there!

I got a pseudo code and don't know how to apply it to R, maybe someone can
help me:

Input: A dataset X, kmax: maximum number of clusters, num_subsamples: number of
subsamples.

Output: S(i; k) - a distribution of similarities between partitions into k
clusters of a reference
clustering and clustering of subsamples; i = 1 to num_subsamples

Requires: T = cluster(X): A hierarchical clustering algorithm
L = cut-tree(T; k): produces a partition with k non-singleton clusters

s(L1;L2): a similarity between two partitions

1: f = 0:8
2: T =cluster(X) fthe reference clustering 
3: for i = 1 to num subsamples do
4: subi =subsamp(X; f) fsub-sample a fraction f of the datag 
5: Ti=cluster(subi)
6: end for
7: for k = 2 to kmax do
8: L1=cut-tree(T; k) fpartition the reference clusteringg 
9: for i = 1 to maximum iterations do
10: L2 =cut-tree(Ti; k)
11: S(i; k) = s(L2;L1) computed only on the patterns of subi.
12: end for
13: end for

I am glad about any help, don't really know what to do!
thanks, regards
Julia
--

> Requires: T = cluster(X): A hierarchical clustering algorithm
> L = cut-tree(T; k): produces a partition with k non-singleton clusters
The functions you'll want to read the documentation to, here, are 
"hclust()" and "cutree()".  They're fairly
straightforward and nicely
documented.

It looks like you are running cutree at multiple heights of the hclust 
object to get different numbers of clusters; am I interpreting that 
correctly?

--elijah
> s(L1;L2): a similarity between two partitions
>
> 1: f = 0:8
> 2: T =cluster(X) fthe reference clustering
> 3: for i = 1 to num subsamples do
> 4: subi =subsamp(X; f) fsub-sample a fraction f of the datag
> 5: Ti=cluster(subi)
> 6: end for
> 7: for k = 2 to kmax do
> 8: L1=cut-tree(T; k) fpartition the reference clusteringg
> 9: for i = 1 to maximum iterations do
> 10: L2 =cut-tree(Ti; k)
> 11: S(i; k) = s(L2;L1) computed only on the patterns of subi.
> 12: end for
> 13: end for
>
> I am glad about any help, don't really know what to do!
> thanks, regards
> Julia
> --
>
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