2. Hierarchical Clustering

Activity 2.01: Comparing k-means with Hierarchical Clustering

Solution:

1. Import the necessary packages from scikit-learn (KMeans, AgglomerativeClustering, and silhouette_score), as follows:

   from sklearn.cluster import KMeans
   from sklearn.cluster import AgglomerativeClustering
   from sklearn.metrics import silhouette_score
   import pandas as pd
   import matplotlib.pyplot as plt

2. Read the wine dataset into the Pandas DataFrame and print a small sample:

   wine_df = pd.read_csv("wine_data.csv")
   print(wine_df.head())

   The output is as follows:

   Figure 2.25: The output of the wine dataset

3. Visualize the wine dataset to understand the data structure:

   plt.scatter(wine_df.values[:,0], wine_df.values[:,1])
   plt.title("Wine Dataset")
   plt.xlabel("OD Reading")
   plt.ylabel("Proline")
   plt.show()

   The output is as follows:

   Figure 2.26: A plot of raw wine data

4. Use the sklearn implementation of k-means on the wine dataset, knowing that...