You could do from sklearn.pipeline import make_pipeline from sklearn.preprocessing import Normalizer from sklearn.cluster import KMeans # (or e.g. MiniBatchKMeans)
spherical_kmeans = make_pipeline(Normalizer(), KMeans(n_clusters=5)) On Tue, Jun 28, 2016 at 12:28 AM, JAGANADH G <[email protected]> wrote: > Hi Fred and Michel, > > Thanks for the reply . I think I git this and am able to run it. > > > Best > Jagan > > > On Mon, Jun 27, 2016 at 1:03 PM, Fred Mailhot <[email protected]> > wrote: > >> Per the example here: >> >> http://scikit-learn.org/stable/auto_examples/text/document_clustering.html >> >> if your inputs are normalized, sklearn's kmeans behaves like sperical >> kmeans (unless I'm misunderstanding something, which is certainly possible, >> caveat lector, &c )... >> On Jun 27, 2016 12:13 PM, "Michael Eickenberg" < >> [email protected]> wrote: >> >>> hmm, not an answer, and off the top of my head: >>> if you normalize your data points to l2 norm equal 1, and then use >>> standard kmeans with euclidean distance (which then amounts to 2 - 2 >>> cos(angle between points)) would this be enough for your purposes? (with a >>> bit of luck there may even be some sort of correspondence) >>> >>> Michael >>> >>> On Monday, June 27, 2016, JAGANADH G <[email protected]> wrote: >>> >>>> Hi , >>>> is there any Python package available for experiment with Sperical >>>> Kmeans ? >>>> >>>> >>>> -- >>>> ********************************** >>>> JAGANADH G >>>> http://jaganadhg.in >>>> *ILUGCBE* >>>> http://ilugcbe.org.in >>>> >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> [email protected] >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > > -- > ********************************** > JAGANADH G > http://jaganadhg.in > *ILUGCBE* > http://ilugcbe.org.in > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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