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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