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<p>As you mention degree as unit for the distance, I assume that you
are working with unprojected data. 1 degree is a huge distance, so
consequently, all points will fall into this area and form one
cluster.</p>
<p>You'll have to project your data and find some meaningful value
for the distance to get different clusters.<br>
</p>
<div class="moz-cite-prefix">On 30.04.21 02:02, krishna Ayyala
wrote:<br>
</div>
<blockquote type="cite"
cite="mid:CADUrCAjMXjf8E9__nXfLE2DS+9+V3ijirrq5ekOSCBjUnyyR_g@mail.gmail.com">
<pre class="moz-quote-pre" wrap="">Nyall,
Thanks for your reply. I have used the "dbscan clustering". In this, the
default values for "Minimum Cluster Size" is 5 and Max distance between
cluster points is 1 degrees. I ran with the same default values. I am
getting output with the layer name "Clusters". This has generated a new
field "Cluster_ID". All the points have same Cluster_ID i.e. 1. This has
also generated new field called "Cluster_Size". All the points have same
cluster size i.e. 1699. Can you please help me on how to get 4 different
cluster ID's.
Regards.
On Thu, Apr 29, 2021 at 4:29 PM Nyall Dawson <a class="moz-txt-link-rfc2396E" href="mailto:nyall.dawson@gmail.com"><nyall.dawson@gmail.com></a> wrote:
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<pre class="moz-quote-pre" wrap="">
On Fri, 30 Apr 2021 at 08:25, krishna Ayyala <a class="moz-txt-link-rfc2396E" href="mailto:ayyalakrishna@gmail.com"><ayyalakrishna@gmail.com></a>
wrote:
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<blockquote type="cite">
<pre class="moz-quote-pre" wrap="">Chris,
Thanks for the reply. Yes, I did run k-means clustering for 4 clusters.
It is creating a new layer called "clusters". This layer has a field called
Cluster_id, ranging from 1 to 4. But, this method is considering all the
points within the circle. I am looking for the points outside the ellipses
to be omitted (should not be considered)
</pre>
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<pre class="moz-quote-pre" wrap="">
In this case "dbscan clustering" is more appropriate.
</pre>
<blockquote type="cite">
<pre class="moz-quote-pre" wrap="">. Also K-means clustering is not generating any polygons/ellipses. We
have to identify a cluster based on the Cluster_ID. I am curious if there
is any tool within Qgis that can produce results similar to the circle with
ellipses?
</pre>
</blockquote>
<pre class="moz-quote-pre" wrap="">
What you could do is dissolve the points based on the cluster_id field,
and then generate convex (or concave) hulls enclosing each set of points.
You won't get ellipses, but you'll get polygons describing the boundaries.
(And it would be relatively straightforward to wrap up these steps into a
single graphical model so that you have one tool which gives the desired
output!).
Nyall
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<pre class="moz-quote-pre" wrap="">
Regards.
On Thu, Apr 29, 2021 at 3:56 PM chris hermansen <a class="moz-txt-link-rfc2396E" href="mailto:clhermansen@gmail.com"><clhermansen@gmail.com></a>
wrote:
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<pre class="moz-quote-pre" wrap="">Krishna and list,
On Thu, Apr 29, 2021 at 2:51 PM krishna Ayyala <a class="moz-txt-link-rfc2396E" href="mailto:ayyalakrishna@gmail.com"><ayyalakrishna@gmail.com></a>
wrote:
</pre>
<blockquote type="cite">
<pre class="moz-quote-pre" wrap="">Hello,
I have a circle in which there are randomly distributed points as
below. Each point is a customer.
[image: image.png]
Is there a tool within QGIS that can automatically generate four
polygons or four ellipses such as below. These polygons are the areas of
maximum concentration of the customers?
[image: image.png]
You could try k-means clustering
</pre>
</blockquote>
<pre class="moz-quote-pre" wrap="">
<a class="moz-txt-link-freetext" href="https://docs.qgis.org/3.4/en/docs/user_manual/processing_algs/qgis/vectoranalysis.html#k-means-clustering">https://docs.qgis.org/3.4/en/docs/user_manual/processing_algs/qgis/vectoranalysis.html#k-means-clustering</a>
--
Chris Hermansen · clhermansen "at" gmail "dot" com
C'est ma façon de parler.
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