Chapter 3: Neighborhood Approaches and DBSCAN

Activity 4: Implement 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 3.14: Plot of generated data

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

   def scratch_DBSCAN(x, eps, min_pts):
       """
       param x (list of vectors): your dataset to be clustered
       param eps (float): neigborhood radius threshold
       param min_pts (int): minimum number of points threshold for a nieghborhood to be a cluster
       """
        # Build a label holder that is comprised of all 0s
       labels = [0]* x.shape[0]
       # Arbitrary starting...