title: Adjust DBSCAN in python so that it reads in my dataset
body: <p>I have been using this code to try and cluster my latitude and longitude points from my file. However I think that it is actually creating random points instead of actually using my own points to find clusters.</p>
  
  <p>I am still unsure of how this algorithm is working. If anyone can shed some light on how I would read in the data properly instead of creating random points which is what I think make_blobs is doing it would be much appreciated</p>
  
  <pre><code>print(__doc__)
  
  import numpy as np
  
  from sklearn.cluster import DBSCAN
  from sklearn import metrics
  from sklearn.datasets.samples_generator import make_blobs
  from sklearn.preprocessing import StandardScaler
  
  
  ##############################################################################
  # Generate sample data
  input = np.genfromtxt(open("dataset_import_noaddress.csv","rb"),delimiter=",", skip_header=1)
  coordinates = np.delete(input, [0,1], 1)
  
  X, labels_true = make_blobs(n_samples=750, centers=coordinates, cluster_std=0.4,
                              random_state=0)
  
  X = StandardScaler().fit_transform(X)
  
  ##############################################################################
  # Compute DBSCAN
  db = DBSCAN(eps=0.3, min_samples=10).fit(X)
  core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
  core_samples_mask[db.core_sample_indices_] = True
  labels = db.labels_
  
  # Number of clusters in labels, ignoring noise if present.
  n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
  
  print('Estimated number of clusters: %d' % n_clusters_)
  print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
  print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
  print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
  print("Adjusted Rand Index: %0.3f"
        % metrics.adjusted_rand_score(labels_true, labels))
  print("Adjusted Mutual Information: %0.3f"
        % metrics.adjusted_mutual_info_score(labels_true, labels))
  print("Silhouette Coefficient: %0.3f"
        % metrics.silhouette_score(X, labels))
  
  ##############################################################################
  # Plot result
  import matplotlib.pyplot as plt
  
  # Black removed and is used for noise instead.
  unique_labels = set(labels)
  colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
  for k, col in zip(unique_labels, colors):
      if k == -1:
          # Black used for noise.
          col = 'k'
  
      class_member_mask = (labels == k)
  
      xy = X[class_member_mask &amp; core_samples_mask]
      plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
               markeredgecolor='k', markersize=14)
  
      xy = X[class_member_mask &amp; ~core_samples_mask]
      plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
               markeredgecolor='k', markersize=6)
  
  plt.title('Estimated number of clusters: %d' % n_clusters_)
  plt.show()
  </code></pre>
  
  <p>I know this may be a trivial question, but I am really just hoping to find out if there is a way to simply use my dataset, instead of creating a random dataset. Coordinates contains a matrix of all my points</p>