Hi Sebastian,
Thanks for the explanations. But what am seeing is that if I feed a data
with 3K features/attributes the "cluster_centers_ method will give 3K
values for each clusters. My objective is to give a name to each cluster
based on the feature names closest to the centroids .
Best
Jagan
On Fri, Apr 29, 2016 at 5:09 PM, Sebastian Raschka <se.rasc...@gmail.com>
wrote:
> Hi, Jaganadh,
>
> it looks like you ran k-means on a 2-dimensional dataset (i.e., a dataset
> with 2 feature variables) and k=3. Thus, the results mean that these three
> cluster centers (or “centroids”) are the centers of the 3 clusters that
> k-means attempted to discover. Or in other words, there are 3 globular
> spheres with its center points
>
> > [ 1.01505989, -0.70632886], [ 0.33475124, 0.89126382], and
> [-1.287003 , -0.43512572]
>
> and each of the training points will be closest to one of these centroids,
> which defines the cluster a training point has been assigned to. Here’s a
> figure of how it could look like when plotted in a 2D scatterplot,
> hopefully, it makes it more clear:
> https://raw.githubusercontent.com/rasbt/mlxtend/master/docs/sources/user_guide/cluster/Kmeans_files/Kmeans_17_0.png
>
> Best,
> Sebastian
>
>
> > On Apr 29, 2016, at 7:59 PM, JAGANADH G <jagana...@gmail.com> wrote:
> >
> > Hi ,
> > After performing clustering, the cluster centers can be extracted via
> .cluster_centers_.
> >
> > A sample result is
> >
> > kmeans.cluster_centers_
> > array([[ 1.01505989, -0.70632886],
> > [ 0.33475124, 0.89126382],
> > [-1.287003 , -0.43512572]])
> >
> > How can I interpret these values.
> >
> > Can somebody help me understanding this document bit detail
> >
> > cluster_centers_ : array, [n_clusters, n_features]
> > Coordinates of cluster centers
> >
> > --
> > **********************************
> > JAGANADH G
> > http://jaganadhg.in
> > ILUGCBE
> > http://ilugcbe.org.in
> >
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--
**********************************
JAGANADH G
http://jaganadhg.in
*ILUGCBE*
http://ilugcbe.org.in
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