[scikit-learn] Getting the indexes of the data points after clustering using Kmeans

[Christian Braune]

Hi,

if you have your original points stored in a numpy array, you can get all
points from a cluster i by doing the following:

cluster_points = points[kmeans.labels_ == i]

"kmeans.labels_" contains a list labels for each point.
"kmeans.labels_ == i" creates a mask that selects only those points that
belong to cluster i
and the whole line then gives you the points, finally.

BTW: the fit method has the raw points as input parameter, not the distance
matrix.

Regards,
 Christian

prince gosavi <princegosavi12 at gmail.com> schrieb am Mi., 21. Feb. 2018 um
11:16 Uhr:

> Hi,
> I have applied Kmeans clustering using the scikit library from
>
> kmeans=KMeans(max_iter=4,n_clusters=10,n_init=10).fit(euclidean_dist)
>
>  After applying the algorithm.I would like to get the data points in the
> clusters so as to further use them to apply a model.
>
> Example:
> kmeans.cluster_centers_[1]
>
> gives me distance array of all the data points.
>
> Is there any way around this available in scikit so as to get the data
> points id/index.
>
> Regards
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