If you’re l2 norming your data, you’re making it live on the surface of a hypershere. That surface will have a high density of points and may not have areas of low density, in which case the entire surface could be recognized as a single cluster if epsilon is high enough and min neighbors is low enough. I’d suggest not using l2 norm with DBSCAN. On Sat, May 12, 2018 at 7:27 AM Mauricio Reis <rei...@gmail.com> wrote:
> The DBScan "fit" method (in scikit-learn v0.19.1) is freezing my computer > without any warning message! > > I am using WinPython 3.6.5 64 bit. > > The method works normally with the original data, but freezes when I use > the normalized data (between 0 and 1). > > What should I do? > > Att., > Mauricio Reis > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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