3. Neighborhood Approaches and DBSCAN

Activity 3.01: Implementing DBSCAN from Scratch

Solution:

1. Generate a random cluster dataset as follows:

   from sklearn.cluster import DBSCAN
   from sklearn.datasets import make_blobs
   import matplotlib.pyplot as plt
   import numpy as np
   %matplotlib inline
   X_blob, y_blob = make_blobs(n_samples=500, \
                               centers=4, n_features=2, \
                               random_state=800)

2. Visualize the generated data:

   plt.scatter(X_blob[:,0], X_blob[:,1])
   plt.show()

   The output is as follows:

   Figure 3.15: Plot of the data generated

3. Create functions from scratch that allow you to call DBSCAN on a dataset:

   Activity3.01.ipynb

   def scratch_DBSCAN(x, eps, min_pts...