scikit-learn: Finding the features that contribute to each KMeans cluster

Question

Say you have 10 features you are using to create 3 clusters. Is there a way to see the level of contribution each of the features have for each of the clusters?

What I w

Answer 1

You can do it this way:

>>> import numpy as np
>>> import sklearn.cluster as cl
>>> data = np.array([99,1,2,103,44,63,56,110,89,7,12,37])
>>> k_means = cl.KMeans(init='k-means++', n_clusters=3, n_init=10)
>>> k_means.fit(data[:,np.newaxis]) # [:,np.newaxis] converts data from 1D to 2D
>>> k_means_labels = k_means.labels_
>>> k1,k2,k3 = [data[np.where(k_means_labels==i)] for i in range(3)] # range(3) because 3 clusters
>>> k1
array([44, 63, 56, 37])
>>> k2
array([ 99, 103, 110,  89])
>>> k3
array([ 1,  2,  7, 12])