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After applying clustering algorithm I need to extract those data which exists centre of the cluster and which exists border of the cluster. How could I do this by using python.

I use k-means clustering algorithm and divide those data into 19 cluster. I am also using scikit learn library.

Edited:

Here's my plot after clustering: enter image description here

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  • $\begingroup$ Can you share a plot of your data?We might need to manually trim the data(not quite sure) as we will have different centroids coordinates.. $\endgroup$
    – Aditya
    Feb 25, 2018 at 17:36
  • $\begingroup$ As I am new in clustering and I have 682 instances with 35 features that's why I don't know how I can plot? If I provides centroids willbe it helpful ? $\endgroup$
    – IS2057
    Feb 25, 2018 at 17:44

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You can use numpy.where documentation can be fouind here : https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.where.html

You can define a function to extract your data ID somthing like this

def ClusterIndicesNumpy(clustNum, labels_array): 
    return np.where(labels == cluster)[0]

To get samples from cluster 3 for example:

ClusterIndicesNumpy(3, km.labels_)
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  • $\begingroup$ I need those data in every(19) cluster that are very close to the centre. By using numpy.where how could I identify those data ? $\endgroup$
    – IS2057
    Feb 26, 2018 at 18:05

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