> 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
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
clusteri
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