title: KMeans clustering from all possible combinations of 2 columns not producing correct output
body: <p>I have a 4 column dataframe which I extracted from the iris dataset. I use kmeans to plot 3 clusters from all possible combinations of 2 columns.</p>
  <p>However, there seems to be something wrong with the output, especially since the cluster centers are not placed at the center of the clusters. I have provided examples of the output. Only cluster_1 seems OK but the other 3 look completely wrong<a href="https://i.stack.imgur.com/UAQtu.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/UAQtu.png" alt="enter image description here" /></a> <a href="https://i.stack.imgur.com/1rRut.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/1rRut.png" alt="enter image description here" /></a> <a href="https://i.stack.imgur.com/JW4yJ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/JW4yJ.png" alt="enter image description here" /></a> <a href="https://i.stack.imgur.com/1O9J4.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/1O9J4.png" alt="enter image description here" /></a>.</p>
  <p>How best can I fix my clustering? This is the sample code I am using</p>
  <pre><code>import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  from sklearn.cluster import KMeans
  import itertools
  
  df = pd.read_csv('iris.csv')
  df_columns = ['column_a', 'column_b', 'column_c', 'column_d']
  n_clusters=3
  kmeans = KMeans(n_clusters=n_clusters, init = 'k-means++', max_iter=200)
  kmeans = kmeans.fit(df)
  centroids = kmeans.cluster_centers_
  cluster_labels = kmeans.labels_
  for i in itertools.combinations(df_columns, 2):
      fig, ax = plt.subplots(figsize=(12, 8))
      fig=plt.figure()
      ax.scatter(df[i[0]].values, df[i[1]].values, c=cluster_labels , cmap='viridis', edgecolor='k', s=20, alpha = 0.5)
      ax.scatter(centroids[:, 0], centroids[:, 1],s = 20, c = 'black', marker='*')
      plt.show()
  </code></pre>
  <p><strong>Dataset used:</strong></p>
  <pre><code>**column_a**,**column_b**,**column_c**,**column_d**
  5.1,3.5,1.4,0.2
  4.9,3.0,1.4,0.2
  4.7,3.2,1.3,0.2
  4.6,3.1,1.5,0.2
  5.0,3.6,1.4,0.2
  5.4,3.9,1.7,0.4
  4.6,3.4,1.4,0.3
  5.0,3.4,1.5,0.2
  4.4,2.9,1.4,0.2
  4.9,3.1,1.5,0.1
  5.4,3.7,1.5,0.2
  4.8,3.4,1.6,0.2
  4.8,3.0,1.4,0.1
  4.3,3.0,1.1,0.1
  5.8,4.0,1.2,0.2
  5.7,4.4,1.5,0.4
  5.4,3.9,1.3,0.4
  5.1,3.5,1.4,0.3
  5.7,3.8,1.7,0.3
  5.1,3.8,1.5,0.3
  5.4,3.4,1.7,0.2
  5.1,3.7,1.5,0.4
  4.6,3.6,1.0,0.2
  5.1,3.3,1.7,0.5
  4.8,3.4,1.9,0.2
  5.0,3.0,1.6,0.2
  5.0,3.4,1.6,0.4
  5.2,3.5,1.5,0.2
  5.2,3.4,1.4,0.2
  4.7,3.2,1.6,0.2
  4.8,3.1,1.6,0.2
  5.4,3.4,1.5,0.4
  5.2,4.1,1.5,0.1
  5.5,4.2,1.4,0.2
  4.9,3.1,1.5,0.1
  5.0,3.2,1.2,0.2
  5.5,3.5,1.3,0.2
  4.9,3.1,1.5,0.1
  4.4,3.0,1.3,0.2
  5.1,3.4,1.5,0.2
  5.0,3.5,1.3,0.3
  4.5,2.3,1.3,0.3
  4.4,3.2,1.3,0.2
  5.0,3.5,1.6,0.6
  5.1,3.8,1.9,0.4
  4.8,3.0,1.4,0.3
  5.1,3.8,1.6,0.2
  4.6,3.2,1.4,0.2
  5.3,3.7,1.5,0.2
  5.0,3.3,1.4,0.2
  7.0,3.2,4.7,1.4
  6.4,3.2,4.5,1.5
  6.9,3.1,4.9,1.5
  5.5,2.3,4.0,1.3
  6.5,2.8,4.6,1.5
  5.7,2.8,4.5,1.3
  6.3,3.3,4.7,1.6
  4.9,2.4,3.3,1.0
  6.6,2.9,4.6,1.3
  5.2,2.7,3.9,1.4
  5.0,2.0,3.5,1.0
  5.9,3.0,4.2,1.5
  6.0,2.2,4.0,1.0
  6.1,2.9,4.7,1.4
  5.6,2.9,3.6,1.3
  6.7,3.1,4.4,1.4
  5.6,3.0,4.5,1.5
  5.8,2.7,4.1,1.0
  6.2,2.2,4.5,1.5
  5.6,2.5,3.9,1.1
  5.9,3.2,4.8,1.8
  6.1,2.8,4.0,1.3
  6.3,2.5,4.9,1.5
  6.1,2.8,4.7,1.2
  6.4,2.9,4.3,1.3
  6.6,3.0,4.4,1.4
  6.8,2.8,4.8,1.4
  6.7,3.0,5.0,1.7
  6.0,2.9,4.5,1.5
  5.7,2.6,3.5,1.0
  5.5,2.4,3.8,1.1
  5.5,2.4,3.7,1.0
  5.8,2.7,3.9,1.2
  6.0,2.7,5.1,1.6
  5.4,3.0,4.5,1.5
  6.0,3.4,4.5,1.6
  6.7,3.1,4.7,1.5
  6.3,2.3,4.4,1.3
  5.6,3.0,4.1,1.3
  5.5,2.5,4.0,1.3
  5.5,2.6,4.4,1.2
  6.1,3.0,4.6,1.4
  5.8,2.6,4.0,1.2
  5.0,2.3,3.3,1.0
  5.6,2.7,4.2,1.3
  5.7,3.0,4.2,1.2
  5.7,2.9,4.2,1.3
  6.2,2.9,4.3,1.3
  5.1,2.5,3.0,1.1
  5.7,2.8,4.1,1.3
  6.3,3.3,6.0,2.5
  5.8,2.7,5.1,1.9
  7.1,3.0,5.9,2.1
  6.3,2.9,5.6,1.8
  6.5,3.0,5.8,2.2
  7.6,3.0,6.6,2.1
  4.9,2.5,4.5,1.7
  7.3,2.9,6.3,1.8
  6.7,2.5,5.8,1.8
  7.2,3.6,6.1,2.5
  6.5,3.2,5.1,2.0
  6.4,2.7,5.3,1.9
  6.8,3.0,5.5,2.1
  5.7,2.5,5.0,2.0
  5.8,2.8,5.1,2.4
  6.4,3.2,5.3,2.3
  6.5,3.0,5.5,1.8
  7.7,3.8,6.7,2.2
  7.7,2.6,6.9,2.3
  6.0,2.2,5.0,1.5
  6.9,3.2,5.7,2.3
  5.6,2.8,4.9,2.0
  7.7,2.8,6.7,2.0
  6.3,2.7,4.9,1.8
  6.7,3.3,5.7,2.1
  7.2,3.2,6.0,1.8
  6.2,2.8,4.8,1.8
  6.1,3.0,4.9,1.8
  6.4,2.8,5.6,2.1
  7.2,3.0,5.8,1.6
  7.4,2.8,6.1,1.9
  7.9,3.8,6.4,2.0
  6.4,2.8,5.6,2.2
  6.3,2.8,5.1,1.5
  6.1,2.6,5.6,1.4
  7.7,3.0,6.1,2.3
  6.3,3.4,5.6,2.4
  6.4,3.1,5.5,1.8
  6.0,3.0,4.8,1.8
  6.9,3.1,5.4,2.1
  6.7,3.1,5.6,2.4
  6.9,3.1,5.1,2.3
  5.8,2.7,5.1,1.9
  6.8,3.2,5.9,2.3
  6.7,3.3,5.7,2.5
  6.7,3.0,5.2,2.3
  6.3,2.5,5.0,1.9
  6.5,3.0,5.2,2.0
  6.2,3.4,5.4,2.3
  5.9,3.0,5.1,1.8
  </code></pre>