Re: Add low-memory special case for Euclidean single-linkage clustering

On Wed, 05 Jul 2023 20:39:37 +0000
Julien Jerphanion <[email protected]> wrote:

> I think that `sklearn.cluster.AgglomerativeClustering`¹ implements
> what you propose.
> 
> This implementation also supports other dissimilarities such as the
> cosine dissimilarity and the Manhattan distance.

Hi Julien

Thanks for the pointer! I just tried to run AgglomerativeClustering with
linkage="single" on 10k, 20k, 40k, 80k 2-d points and the runtimes
indicate a super-quadratic time complexity: 0.33, 1.45, 5.96, 24.2 s.

Given that my use case involves around 200k points in 3-d,
I'd like something closer to O(N log N) time - is that possible with
AgglomerativeClustering?

Cheers,
Mathias
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