In short, no, monkey patching cosine_similarity in place of euclidean_distances will not work. See for instance this StackOverflow post: http://stats.stackexchange.com/questions/81481/why-does-k-means-clustering-algorithm-use-only-euclidean-distance-metric
You could try out this Kernel KMeans implementation: https://github.com/scikit-learn/scikit-learn/pull/5483 On 3 June 2016 at 05:24, JAGANADH G <[email protected]> wrote: > Hi Team, > I was trying to use cosine similarity with KMeans. The code which I used > to achieve is available here. > https://gist.github.com/jaganadhg/b3f6af86ad99bf6e9bb7be21e5baa1b5 > > Is it the right way to achieve the same ? I know that cosine is not > directly supported in sklearn KMeans. But after skimming through the code I > was thinking that this will work ;-) > > -- > ********************************** > 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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