[scikit-learn] Agglomerative Clustering without knowing number of clusters

[Ariani A]

Dear Shane,
Thanks for your time. But I have to implement it by agglomerative
clustering and cut it when each cluster has at least 40 data points. But I
am not sure how to do cut it. I was guessing maybe it can be done by
cutting the dandrogram? Is it correct? If so, I do not know how to apply
it. Could you give me a point?
Best,
Ariani

On Thu, Jul 6, 2017 at 12:32 PM, Shane Grigsby <shane.grigsby at colorado.edu>
wrote:

> This sounds like it may be a problem more amenable to either DBSCAN or
> OPTICS. Both algorithms don't require a priori knowledge of the number of
> clusters, and both let you specify a minimum point membership threshold for
> cluster membership. The OPTICS algorithm will also produce a dendrogram
> that you can cut for sub clusters if need be.
>
> DBSCAN is part of the stable release and has been for some time; OPTICS is
> pending as a pull request, but it's stable and you can try it if you like:
>
> https://github.com/scikit-learn/scikit-learn/pull/1984
>
> Cheers,
> Shane
>
>
> On 06/30, Ariani A wrote:
>
>> I want to perform agglomerative clustering, but I have no idea of number
>> of
>> clusters before hand. But I want that every cluster has at least 40 data
>> points in it. How can I apply this to sklearn.agglomerative clustering?
>> Should I use dendrogram and cut it somehow? I have no idea how to relate
>> dendrogram to this and cutting it out. Any help will be appreciated!
>>
>
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>
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> *Cooperative Institute for Research in Environmental Sciences (CIRES)*
> *University of Colorado at Boulder*
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