Dear Shane, Sorry bothering you! Is the "precomputed" and "distance matrix" you are talking about, are about "DBSCAN" ? Thanks, Best.
On Thu, Jul 13, 2017 at 7:03 PM, Ariani A <b.noush...@gmail.com> wrote: > Dear Shane, > Thanks for your prompt answer. > Do you mean that for DBSCAN there is no need to feed other parameters? Do > I just call the function or I have to manipulate the code? > P.S. I was not able to find the DBSCAN code on github. > Looking forward to hearing from you. > Best, > -Noushin > > On Thu, Jul 13, 2017 at 5:38 PM, Shane Grigsby <shane.grig...@colorado.edu > > wrote: > >> 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! >>>>> >>>>> >>>> _______________________________________________ >>>> >>>>> 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 >>>> >>>> >> _______________________________________________ >>> 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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