Hi, Thanks for your hint It just saved my day. Regards, Rajkumar On Wed, Feb 21, 2018 at 4:28 PM, Christian Braune < christian.braune79@gmail.com> wrote:

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@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.

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