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 >
KMeans_Cluster_Circle.ipynb
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