[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.
Andrew
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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, PhDLinkedIn ProfileResearchGate ProfileOpen Researcher and
Contributor ID (ORCID)Github Profile
Personal WebsiteI 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:
HiI 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
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