class_docstr: A density based clustering method that expands clusters from 
  samples that have more neighbors within a radius specified by eps
  than the value min_samples.
  
  Parameters:
  -----------
  eps: float
      The radius within which samples are considered neighbors
  min_samples: int
      The number of neighbors required for the sample to be a core point. 
code: def __init__(self, eps=1, min_samples=5):
      self.eps = eps
      self.min_samples = min_samples
  
file_path: ML-From-Scratch/mlfromscratch/unsupervised_learning/dbscan.py
imports: from __future__ import print_function, division
  import numpy
  from mlfromscratch.utils import Plot, euclidean_distance, normalize
label_desc: Use image node_id 1 to create a new DBSCAN object  with example: obj = DBSCAN(eps, min_samples)