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.grig...@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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*Cooperative Institute for Research in Environmental Sciences (CIRES)*
*University of Colorado at Boulder*
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