title: Scikit learn: Applying Mean Shift on a multi-dimensional dataset
body: <p>I have a dataset which has 14 different features/columns and 4328 rows, values of which I have then processed and converted into a NumPy array of shape (4328, 14). I have then applied Mean Shift on this NumPy array to train my model, which segregates the data points into 29 different clusters. </p>
  
  <p>Cluster centres:</p>
  
  <pre><code>array([[ 0.00000000e+00,  2.88896062e+02,  2.78953471e+02,
           2.08648004e+02,  2.12223611e+02,  5.38985939e+01,
           3.71283150e-01,  5.70311771e+03,  4.54253094e-01,
           1.30592925e+00,  6.64259488e+00,  3.82481843e+00,
           6.43865296e+00,  6.43865296e+00],
         [ 0.00000000e+00,  2.83183908e+02,  9.48864664e+01,
           3.59258621e+03,  9.05744253e+01,  8.35206117e+00,
           4.13793103e-01,  5.70172414e+03,  2.78249425e-01,
           8.88868966e-01,  6.63727816e+00,  4.84751149e+00,
           6.61705172e+00,  6.61705172e+00],
         [ 0.00000000e+00,  3.15511628e+02,  7.55761355e+01,
           6.52134884e+03,  7.04900000e+01,  6.69296631e+00,
           3.72093023e-01,  5.69984767e+03,  3.52367442e-01,
           9.50423256e-01,  6.81103721e+00,  2.70016977e+00,
           3.48411628e+00,  3.48411628e+00],
         [ 0.00000000e+00,  2.98297297e+02,  4.95190674e+01,
           9.43194595e+03,  4.64532432e+01,  4.89748830e+00,
           3.24324324e-01,  5.69470405e+03,  1.71972973e-01,
           1.21458649e+00,  6.85496486e+00,  3.54600000e+00,
           5.62750811e+00,  5.62750811e+00],
         [ 0.00000000e+00,  3.60428571e+02,  3.22145995e+03,
           9.85714286e+00,  3.24273036e+03, -6.35189676e-01,
           4.64285714e-01,  5.65968214e+03, -2.39050000e-01,
           7.49132143e-01,  6.57582857e+00, -2.07893214e+00,
          -6.82446429e-01, -6.82446429e-01],
         [ 0.00000000e+00,  2.48600000e+02,  4.35963021e+01,
           1.18772000e+04,  4.21820000e+01,  3.25541197e+00,
           4.00000000e-01,  5.69281500e+03, -4.94350000e-01,
          -1.41250000e-01,  7.01363000e+00, -7.76800000e-02,
           2.37982000e+00,  2.37982000e+00],
         [ 0.00000000e+00,  2.56777778e+02,  3.86608797e+01,
           1.48944444e+04,  3.43100000e+01,  1.36524043e+01,
           2.22222222e-01,  5.70588333e+03, -4.92000000e-02,
           8.88366667e-01,  6.78814444e+00,  5.58971111e+00,
           6.56455556e+00,  6.56455556e+00],
         [ 0.00000000e+00,  3.14111111e+02,  4.78123643e+01,
           2.02325556e+04,  4.67500000e+01,  4.74006148e+00,
           5.55555556e-01,  5.70420556e+03, -2.40100000e-01,
           8.96300000e-01,  7.09418889e+00,  6.68292222e+00,
           1.12132667e+01,  1.12132667e+01],
         [ 0.00000000e+00,  3.47200000e+02,  3.63744453e+01,
           5.02000000e+04,  3.45700000e+01,  4.97221480e+00,
           8.00000000e-01,  5.67206000e+03, -9.79280000e-01,
          -1.08820000e-01,  7.67404000e+00,  1.17406000e+00,
           1.44780600e+01,  1.44780600e+01],
         [ 0.00000000e+00,  5.46000000e+02,  1.04748000e+04,
           5.66666667e+00,  1.02684667e+04,  2.01687216e+00,
           3.33333333e-01,  5.72818333e+03,  5.43600000e-01,
           1.35213333e+00,  5.60560000e+00,  3.07716667e+00,
           2.22003333e+00,  2.22003333e+00],
         [ 0.00000000e+00,  2.09000000e+02,  2.39866667e+02,
           1.17000000e+02,  2.33150000e+02,  1.67530023e+00,
           1.00000000e+00,  9.13930000e+03, -1.69290000e+00,
          -7.47800000e-01,  2.30790000e+00,  7.06666667e-01,
           1.86860000e+00,  1.86860000e+00],
         [ 0.00000000e+00,  2.01666667e+02,  6.86686111e+01,
           2.57380000e+04,  6.56333333e+01,  5.85024181e+00,
           3.33333333e-01,  5.75526667e+03,  1.19680000e+00,
           2.18410000e+00,  6.13906667e+00,  1.75683667e+01,
           1.90339000e+01,  1.90339000e+01],
         [ 0.00000000e+00,  5.08000000e+02,  4.60818500e+04,
           4.00000000e+00,  4.42663500e+03,  9.41967667e+02,
           5.00000000e-01,  5.73742500e+03, -2.17150000e-01,
           1.11570000e+00,  6.81375000e+00,  2.84170000e+00,
           1.07105000e+00,  1.07105000e+00],
         [ 0.00000000e+00,  5.15000000e+02,  1.23800000e+03,
           2.00000000e+00,  3.66200000e+01,  3.28066630e+03,
           0.00000000e+00,  5.70330000e+03,  2.96260000e+00,
           2.53060000e+00,  6.56880000e+00,  2.56620000e+00,
           5.00280000e+00,  5.00280000e+00],
         [ 0.00000000e+00,  1.53000000e+02,  2.67980246e+01,
           2.50000000e+05,  2.46500000e+01,  8.71409574e+00,
           1.00000000e+00,  5.70805000e+03, -9.63100000e-01,
           4.70000000e-01,  6.79200000e+00, -5.11360000e+00,
           8.20730000e+00,  8.20730000e+00],
         [ 0.00000000e+00,  5.74000000e+02,  2.67405322e+01,
           4.10020000e+04,  2.49200000e+01,  7.30550630e+00,
           1.00000000e+00,  5.73125000e+03,  2.08130000e+00,
           3.34910000e+00,  6.92330000e+00,  5.08680000e+00,
           8.58970000e+00,  8.58970000e+00],
         [ 0.00000000e+00,  5.22000000e+02,  1.00364364e+02,
           3.75630000e+04,  4.90300000e+01,  1.04699906e+02,
           1.00000000e+00,  5.71880000e+03,  7.04600000e-01,
           2.16130000e+00,  5.72310000e+00, -3.00900000e-01,
           1.32520000e+00,  1.32520000e+00],
         [ 0.00000000e+00,  3.46000000e+02,  2.24756530e+02,
           1.27403000e+05,  2.22800000e+02,  8.78155326e-01,
           1.00000000e+00,  5.70805000e+03, -9.63100000e-01,
           4.70000000e-01,  6.79200000e+00,  2.50200000e-01,
           5.96300000e+00,  5.96300000e+00],
         [ 0.00000000e+00,  3.09000000e+02,  4.50972829e+01,
           3.50000000e+04,  4.33000000e+01,  4.15076872e+00,
           0.00000000e+00,  5.67600000e+03,  9.75300000e-01,
           6.17300000e-01,  6.62310000e+00,  4.01550000e+01,
           4.19152000e+01,  4.19152000e+01],
         [ 0.00000000e+00,  3.46000000e+02,  2.26916384e+02,
           1.00000000e+05,  2.24950000e+02,  8.74142476e-01,
           1.00000000e+00,  5.65215000e+03, -1.88000000e-01,
           7.87500000e-01,  7.94750000e+00, -3.13200000e-01,
           6.47550000e+00,  6.47550000e+00],
         [ 0.00000000e+00,  3.46000000e+02,  2.20191000e+02,
           2.75000000e+05,  2.31950000e+02, -5.06962715e+00,
           1.00000000e+00,  5.70460000e+03, -8.96800000e-01,
          -3.83300000e-01,  5.95260000e+00,  5.14140000e+00,
           7.58010000e+00,  7.58010000e+00],
         [ 0.00000000e+00,  2.18000000e+02,  1.69836215e+02,
           6.00000000e+04,  1.73550000e+02, -2.13989340e+00,
           1.00000000e+00,  5.74695000e+03,  2.21600000e-01,
          -2.66200000e-01,  5.37060000e+00,  4.42260000e+00,
           1.03538000e+01,  1.03538000e+01],
         [ 0.00000000e+00,  9.10000000e+01,  5.03828125e+01,
           3.20000000e+04,  4.85000000e+01,  3.88208763e+00,
           0.00000000e+00,  5.71880000e+03,  7.04600000e-01,
           2.16130000e+00,  5.72310000e+00,  7.97870000e+00,
           1.43018000e+01,  1.43018000e+01],
         [ 0.00000000e+00,  1.82000000e+02,  3.66395435e+01,
           5.40000000e+04,  3.63500000e+01,  7.96543380e-01,
           1.00000000e+00,  5.67605000e+03, -1.73390000e+00,
          -2.81400000e-01,  8.15350000e+00, -2.00800000e+00,
           1.52570000e+00,  1.52570000e+00],
         [ 0.00000000e+00,  3.43000000e+02,  2.31617647e+01,
           1.70000000e+04,  2.16500000e+01,  6.98274691e+00,
           0.00000000e+00,  5.67600000e+03,  9.75300000e-01,
           6.17300000e-01,  6.62310000e+00,  2.45333000e+01,
           2.12987000e+01,  2.12987000e+01],
         [ 0.00000000e+00,  2.18000000e+02,  1.63871636e+02,
           1.19500000e+05,  1.61950000e+02,  1.18656127e+00,
           1.00000000e+00,  5.64800000e+03, -2.77500000e-01,
          -1.23880000e+00,  7.32370000e+00, -6.76500000e-01,
          -7.47950000e+00, -7.47950000e+00],
         [ 0.00000000e+00,  3.46000000e+02,  2.24871313e+02,
           7.25970000e+04,  2.22800000e+02,  9.29673637e-01,
           1.00000000e+00,  5.70805000e+03, -9.63100000e-01,
           4.70000000e-01,  6.79200000e+00,  2.50200000e-01,
           5.96300000e+00,  5.96300000e+00],
         [ 0.00000000e+00,  5.70000000e+01,  1.02000000e+01,
           2.35008000e+05,  1.05000000e+01, -2.85714286e+00,
           1.00000000e+00,  5.70460000e+03, -8.96800000e-01,
          -3.83300000e-01,  5.95260000e+00, -3.77360000e+00,
           2.51260000e+00,  2.51260000e+00],
         [ 0.00000000e+00,  2.10000000e+01,  1.19055525e+01,
           4.15000000e+05,  1.14000000e+01,  4.43467132e+00,
           1.00000000e+00,  5.67605000e+03, -1.73390000e+00,
          -2.81400000e-01,  8.15350000e+00, -1.69065000e+01,
          -2.84830000e+01, -2.84830000e+01]]))
  </code></pre>
  
  <p>Now, I tried plotting these clusters in a 2D plane which then produced this plot:
  <a href="https://i.stack.imgur.com/Fu9YZ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Fu9YZ.png" alt="enter image description here"></a></p>
  
  <p>Now, I'm not really sure why my clusters along with the various data points were plotted in a single line, with the value of X-axis for each coordinate being 0. Am I missing something here? Is there a different way I should be preprocessing my dataset if I want to cluster them into different clusters?</p>
  
  <p>Edit 1:
  Code used to plot the above graph (<code>clf</code> is the name of my model object):</p>
  
  <pre><code>labels = clf.labels_
  cluster_centers = clf.cluster_centers_
  n_clusters_ = len(np.unique(labels))
  colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
  for k, col in zip(range(n_clusters_), colors):
      my_members = labels == k
      cluster_center = cluster_centers[k]
      plt.plot(X[my_members, 0], X[my_members, 1], col + '.')
      plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
               markeredgecolor='k', markersize=14)
  plt.title('Estimated number of clusters: %d' % n_clusters_)
  plt.show()
  </code></pre>