I've added this PR, and I addressed in the comments some of your concerns
(publications, comparison to affinity propagation, etc).
https://github.com/scikit-learn/scikit-learn/pull/9329
I'd love for you to review, since this is my first PR in the scikit learn
repository
On Wed, Jul 12, 2017 at 1
If this is the first time you contribute, please make sure to
carefully read the contributors guide till the end:
http://scikit-learn.org/stable/developers/contributing.html
In particular, make sure to follow the estimators API conventions for
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ِDear Uri,
Thanks. I just have a pairwise distance matrix and I want to implement it
so that each cluster has at least 40 data points. (in Agglomerative).
Does it work?
Thanks,
-Ariani
On Tue, Jul 11, 2017 at 1:54 PM, Uri Goren wrote:
> Take a look at scipy's fcluster function.
> If M is a matri
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_dista
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 clustering?
Should I use dendrogram and cut it somehow? I have no idea how to rela
You don't need our permission to submit a PR, go ahead! We welcome PRs.
On Mon, Jul 10, 2017 at 9:36 PM, Uri Goren wrote:
> I have,
> The only criterion that I am unsure about is the number citations.
>
> In the literature Markov clustering is usually compared to affinity
> prolongation, which a
