>Mebbe principal components analysis would suggest an >ellipsoid containing "most" of the points in a "cloud".
Sorry I didn't understand. Can you explain more? Regards, Mahmood On Wed, Dec 9, 2020 at 8:55 PM The Helmbolds via scikit-learn < scikit-learn@python.org> wrote: > [scikit-learn] Drawing contours in KMeans4 > > > Mebbe principal components analysis would suggest an ellipsoid containing > "most" of the points in a "cloud". > > > > > "You won't find the right answers if you don't ask the right questions!" > (Robert Helmbold, 2013) > > > On Wednesday, December 9, 2020, 12:22:49 PM MST, Andrew Howe < > ahow...@gmail.com> wrote: > > > Ok, I see. Well the attached notebook demonstrates doing this by simply > finding the maximum distance from each centroid to it's datapoints and > drawing a circle using that radius. It's simple, but will hopefully at > least point you in a useful direction. > [image: image.png] > Andrew > > <~~~~~~~~~~~~~~~~~~~~~~~~~~~> > J. Andrew Howe, PhD > LinkedIn Profile <http://www.linkedin.com/in/ahowe42> > ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/> > Open Researcher and Contributor ID (ORCID) > <http://orcid.org/0000-0002-3553-1990> > Github Profile <http://github.com/ahowe42> > Personal Website <http://www.andrewhowe.com> > I live to learn, so I can learn to live. - me > <~~~~~~~~~~~~~~~~~~~~~~~~~~~> > > > On Wed, Dec 9, 2020 at 12:59 PM Mahmood Naderan <mahmood...@gmail.com> > wrote: > > I mean a circle/contour to group the points in a cluster for better > representation. > For example, if there are 6 six clusters, it will be more meaningful to > group large data points in a circle or contour. > > Regards, > Mahmood > > > > > On Wed, Dec 9, 2020 at 11:49 AM Andrew Howe <ahow...@gmail.com> wrote: > > Contours generally indicate a third variable - often a probability > density. Kmeans doesn't provide density estimates, so what precisely would > you want the contours to represent? > > Andrew > > <~~~~~~~~~~~~~~~~~~~~~~~~~~~> > J. Andrew Howe, PhD > LinkedIn Profile <http://www.linkedin.com/in/ahowe42> > ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/> > Open Researcher and Contributor ID (ORCID) > <http://orcid.org/0000-0002-3553-1990> > Github Profile <http://github.com/ahowe42> > Personal Website <http://www.andrewhowe.com> > I live to learn, so I can learn to live. - me > <~~~~~~~~~~~~~~~~~~~~~~~~~~~> > > > On Wed, Dec 9, 2020 at 9:41 AM Mahmood Naderan <mahmood...@gmail.com> > wrote: > > Hi > I use the following code to highlight the cluster centers with some red > dots. > > kmeans = KMeans(n_clusters=6, init='k-means++', max_iter=100, n_init=10, > random_state=0) > pred_y = kmeans.fit_predict(a) > plt.scatter(a[:,0], a[:,1]) > plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], > s=100, c='red') > plt.show() > > I would like to know if it is possible to draw contours over the clusters. > Is there any way for that? > Please let me know if there is a function or option in KMeans. > > Regards, > Mahmood > > > _______________________________________________ > 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 > > _______________________________________________ > 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 > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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