dbscanNonGaussianExample.m

clc;
clear;
close all;

X = [-2.2594e-02  -2.8426e-01
  -6.1004e-02  -1.2529e+00
   1.8012e+00  -4.3953e-02
  -2.3199e-01  -4.3215e-01
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   1.0152e+00  -1.0830e+00
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   6.9906e-01  -2.0681e-01
  -7.5593e-01  -7.1684e-01
  -1.3730e-01  -1.0454e+00
   1.8132e+00  -7.9082e-01
   6.6350e-02   3.4364e-01
  -3.5959e-01   7.4675e-01
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  -1.9181e-01  -1.1711e+00
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   1.1960e+00  -1.1866e+00
   5.9888e-01  -1.6982e-01
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   1.8048e-01  -2.0847e-01
  -9.6554e-01  -1.6057e-01
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  -8.9793e-01   6.1860e-01
   1.4029e-01  -1.2717e+00
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   4.0395e-01   1.0319e+00
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   9.0605e-01  -6.1710e-01
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   1.0894e+00  -1.1487e+00
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   4.3208e-01  -1.6927e+00
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   3.3159e+00  -7.0739e-01
   8.4547e-01   3.5865e+00
   6.9317e-01   3.8918e+00
   8.0404e-01  -3.3257e+00
   1.9650e+00   2.5873e+00
   3.4365e+00   1.8219e+00
   8.9074e-01   3.0885e+00
   1.3192e+00   3.3725e+00
   1.6933e+00  -2.9098e+00
   2.9183e+00   2.6368e+00
   2.1467e+00  -3.1101e+00
   3.5106e+00   2.6571e-01
   3.7817e+00  -5.1672e-01
   3.9366e-01   3.6315e+00
   3.7876e+00   9.9987e-01
   4.6316e-01   3.8581e+00
   2.8852e+00   1.8334e+00
   3.6932e+00  -1.1497e+00
   1.1934e+00  -2.8704e+00
   2.9969e+00  -3.0917e-01
   5.6266e-01   3.6611e+00
   1.0397e+00   2.9162e+00
   2.9713e+00  -2.5004e+00
   3.0736e+00  -2.1952e+00
   2.1191e+00  -3.0135e+00
   3.2304e+00  -1.1271e+00
   4.2136e-01   3.9310e+00
   3.3863e+00   8.3441e-01
   1.5903e-01  -3.7851e+00
   8.6148e-01   3.3707e+00
   2.4500e+00  -2.3769e+00
   3.0894e+00  -4.7063e-01
   2.9916e+00  -1.6288e+00
   3.4119e+00  -1.2034e+00
   2.9100e+00  -2.1200e+00
   3.8424e-01  -3.8942e+00
   3.7757e+00   4.7166e-01
   8.7066e-01   3.2156e+00
   2.7974e+00  -2.2442e+00
   1.0020e+00   3.5294e+00
   2.9193e+00   9.6362e-01
   1.4394e+00  -3.2835e+00
   1.8226e-01  -3.4827e+00
   2.3539e+00   2.2029e+00
   5.3804e-01  -3.8751e+00
   1.9966e+00  -2.2741e+00
   1.7686e+00  -2.9067e+00
   3.9009e+00  -1.8150e-01
   2.3992e+00   2.0427e+00
   3.2850e+00   5.6488e-01
   2.3042e+00  -2.5523e+00
   2.3357e+00  -1.9213e+00
   8.6909e-01  -3.0858e+00
   1.1188e+00   3.8194e+00
   2.9321e+00  -1.0424e+00
   1.1160e+00   3.3635e+00
   2.5811e+00   2.4498e+00
   4.6646e-01   3.5346e+00
   2.0842e+00   3.3019e+00
   2.8357e+00  -1.8114e+00
   1.5592e+00  -3.6037e+00
   2.2012e+00   2.6138e+00
   7.2512e-01   3.6860e+00
   7.5530e-02  -3.9873e+00
   8.2941e-01   3.3298e+00
   1.0345e+00   3.4456e+00
   2.5919e+00  -1.7574e+00
   3.9705e+00  -2.2167e-01
   3.7847e+00   1.0189e+00
   2.1092e+00  -2.1520e+00
   3.5445e+00  -7.6223e-01
   3.7318e+00   1.3234e+00
   3.8866e+00  -2.9519e-01
   3.7695e+00  -2.8410e-01
   1.7586e+00   2.9416e+00
   3.4053e+00  -1.4835e+00
   2.0183e+00   2.3202e+00
   2.5091e+00  -2.5788e+00
   2.1403e+00   2.9343e+00
   2.0782e+00   2.6681e+00
   3.4793e+00  -3.6475e-01
   1.1006e+00   3.2158e+00
   2.8225e+00  -1.0203e+00
   5.5829e-01   3.7016e+00
   2.0665e+00  -2.6634e+00
   1.7900e+00  -3.2289e+00
   2.5794e+00   1.8142e+00
   1.8880e+00  -2.4712e+00
   2.7883e-01   3.0388e+00
   3.2136e+00   1.4619e+00
   2.0484e+00  -2.3879e+00
   2.4439e+00  -2.3100e+00
   7.3675e-01  -3.7768e+00
   5.3547e-01  -3.0894e+00
   9.3167e-02  -3.2047e+00
   1.5647e+00  -2.8067e+00
   3.1307e+00   8.5950e-01
   9.5680e-01  -3.4374e+00
   7.7619e-01  -3.7667e+00
   3.5295e+00   4.3717e-01
   1.6903e+00   2.7718e+00
   2.8744e+00  -1.4138e+00
   3.5804e+00   2.3334e-01
   3.5198e+00   3.5049e-01
   1.3898e+00   2.8153e+00
   1.6927e+00   2.5325e+00
   7.1890e-01  -3.4086e+00
   2.4239e+00  -2.7716e+00
   3.0268e+00  -5.2831e-01
   1.7130e+00  -3.5618e+00
   3.2181e+00  -6.3201e-01
   6.3490e-01   3.6426e+00
   2.5348e+00  -1.8521e+00
   1.1998e+00  -3.7480e+00
   4.6597e-01  -3.6956e+00
   3.6673e+00   7.7248e-01
   3.3341e-01   3.3943e+00
   2.6093e+00   1.9744e+00
   7.8294e-01   3.1852e+00
   3.6653e+00  -1.1211e+00
   8.8452e-01  -3.0822e+00
   2.2008e+00  -2.5728e+00
   1.7767e+00   3.5169e+00
   3.7772e+00   1.2704e+00
   5.2182e-01  -3.3297e+00
   2.2057e+00   2.0449e+00
   3.0381e+00   7.5620e-01
   3.4052e+00  -1.2178e+00];

% Run dbscan clustering algorithm
epsilon = 0.3;
min_pts = 3;
[index] = mi.dbscan(X, epsilon, min_pts);

% Get number of clusters
clusters = max(index);

legends = {};
for i = 0:clusters
  % Get the clusters
  Xi = X(find(index == i), :);

  % Create legends
  if i~=0
    legends{end+1} = ['Cluster #' num2str(i)];
   else
    if ~isempty(Xi)
      legends{end+1} = 'Noise';
    end
  end

  % Plot
  if ~isempty(Xi)
    scatter(Xi(:,1),Xi(:,2));
  end
  hold on;
end

grid on;
legend(legends);
legend('Location', 'NorthEastOutside');