Take a look at scipy's fcluster function.
If M is a matrix of all of your feature vectors, this code snippet should
work.
You need to figure out what metric and algorithm work for you
from sklearn.metrics import pairwise_distance
from scipy.cluster import hierarchy
X = pairwise_distance(M, metric=metric)
Z = hierarchy.linkage(X, algo, metric=metric)
C = hierarchy.fcluster(Z,threshold, criterion="distance")
Best,
Uri Goren
On Tue, Jul 11, 2017 at 7:42 PM, Ariani A <[email protected]> wrote:
> Hi all,
> 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 clusteri
> ng?
> 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!
> I have to use agglomerative clustering!
> Thanks,
> -Ariani
>
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*Uri Goren,Software innovator*
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