Coding a hierarchical clustering algorithm

Let’s learn how we can code a hierarchical algorithm in Python:

1. We will first import AgglomerativeClustering from the sklearn.cluster library, along with the pandas and numpy packages:

   from sklearn.cluster import AgglomerativeClustering
   import pandas as pd
   import numpy as np
   

2. Then we will create 20 data points in a two-dimensional problem space:

   dataset = pd.DataFrame({
       'x': [11, 11, 20, 12, 16, 33, 24, 14, 45, 52, 51, 52, 55, 53, 55, 61, 62, 70, 72, 10],
       'y': [39, 36, 30, 52, 53, 46, 55, 59, 12, 15, 16, 18, 11, 23, 14, 8, 18, 7, 24, 70]
   })
   

3. Then we create the hierarchical cluster by specifying the hyperparameters. Note that a hyperparameter refers to a configuration parameter of a machine learning model that is set before the training process and influences the model’s behavior and performance. We use the fit_predict function to actually process...