Dear Shane, Thanks for your time. But I have to implement it by agglomerative clustering and cut it when each cluster has at least 40 data points. But I am not sure how to do cut it. I was guessing maybe it can be done by cutting the dandrogram? Is it correct? If so, I do not know how to apply it. Could you give me a point? 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! >> > > _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > > -- > *PhD candidate & Research Assistant* > *Cooperative Institute for Research in Environmental Sciences (CIRES)* > *University of Colorado at Boulder* > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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