[Python] K-Means Clustering Output seems wrong, how can it be explained?

I have a custom dataset that I want to partion using kmeans. This is my MCVE:

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
samples = np.array([[3.2736001e+03, 1.7453293e+00], [3.7256001e+03, 5.2359879e-02], [3.2960000e+03, 1.7366025e+00], [3.7112000e+03, 4.3633230e-02],
[3.7136001e+03, 4.3633230e-02], [6.8240002e+02, 1.4137167e+00], [6.9279999e+02, 1.4049901e+00], [3.2944001e+03, 1.7366025e+00], [3.7480000e+03, 6.1086524e-02],
[7.4640002e+02, 1.2217305e-01], [3.2712000e+03, 1.7453293e+00], [7.8320001e+02, 1.3962634e-01], [8.0240002e+02, 1.4835298e-01], [3.7176001e+03, 5.2359879e-02],
[7.1520001e+02, 1.3875368e+00], [7.0079999e+02, 1.3962634e+00], [7.6640002e+02, 1.3962634e-01], [7.3440002e+02, 1.1344640e-01], [3.3272000e+03, 1.7278759e+00],
[7.3840002e+02, 1.3788100e+00], [7.6079999e+02, 1.3089970e-01], [7.6240002e+02, 1.3089970e-01], [7.8160004e+02, 1.3613569e+00], [7.6079999e+02, 1.3700835e+00],
[6.0800000e+02, 1.4224433e+00], [6.3600000e+02, 1.4137167e+00], [6.7040002e+02, 1.4224433e+00], [7.8879999e+02, 1.6580628e-01], [7.3840002e+02, 1.2217305e-01],
[8.1920001e+02, 1.5707964e-01], [7.5760004e+02, 1.3089970e-01], [6.6240002e+02, 9.5993109e-02], [7.7520001e+02, 1.4835298e-01], [6.9040002e+02, 1.3962634e+00],
[3.7544001e+03, 6.1086524e-02], [7.8240002e+02, 1.5707964e-01], [7.1520001e+02, 1.1344640e-01], [7.9840002e+02, 1.3526301e+00], [7.4079999e+02, 1.3788100e+00],
[3.7200000e+03, 5.2359879e-02], [3.7168000e+03, 4.3633230e-02], [7.1760004e+02, 1.3875368e+00], [6.6479999e+02, 1.4049901e+00]])
numCluster = 4
kmeans = KMeans(n_clusters=numCluster).fit(samples)
for i in range(0,numCluster):
res = samples[kmeans.labels_ == i]
plt.scatter(res[:,0], res[:,1])
plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], color='black')


The Output


For me the output of the two left clusters doesn't make any sense. From my understanding of kmeans the centers would move right inside the clusters in the next iteration. But even with the given centers the label assinged doesn't make sense.

What I've checked so far:

• The output looks the same using 'kmeans++'
• Adjusting 'tol'
• Tested opencv implementation of kmeans

Can someone explain this output, and how to fix it?

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