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

[Shane Grigsby]

Hi Ariani,
Yes, you can use a distance matrix-- I think that what you want is 
metric='precomputed', and then X would be your N by N distance matrix.
Hope that helps,
~Shane

On 07/13, Ariani A wrote:
>Dear Shane,
>Thanks for your answer.
>Does DBSCAN works with distance matrix/? I have a distance matrix
>(symmetric matrix which contains pairwise distances). Can you help me? I
>did not find DBSCAN code in that link.
>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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-- 
*PhD candidate & Research Assistant*
*Cooperative Institute for Research in Environmental Sciences (CIRES)*
*University of Colorado at Boulder*