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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