ِ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 <u...@goren4u.com> wrote: > 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 <b.noush...@gmail.com> 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 >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > > -- > > > *Uri Goren,Software innovator* > > *Phone: +972-507-649-650* > > *EMail: u...@goren4u.com <u...@goren4u.com>* > *Linkedin: il.linkedin.com/in/ugoren/ <http://il.linkedin.com/in/ugoren/>* > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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