Practical 7: Hierarchical Clustering

Objective

Understand and apply hierarchical clustering with dendrogram visualization.

Duration

3-4 hours

Prerequisites

• Practicals 1-6 completed

What You’ll Learn

• ✅ Understand linkage methods
• ✅ Perform agglomerative clustering
• ✅ Create and interpret dendrograms
• ✅ Cut dendrograms at optimal height
• ✅ Compare with K-Means

📋 Tasks

1. Hierarchical Clustering

from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.cluster import AgglomerativeClustering

Z = linkage(X, method='ward')
labels = AgglomerativeClustering(n_clusters=3).fit_predict(X)

2. Dendrogram Visualization

dendrogram(Z)
plt.xlabel('Sample index')
plt.ylabel('Distance')
plt.show()

📊 Learning Outcomes

• Apply hierarchical clustering
• Compare linkage methods
• Interpret dendrograms
• Cut dendrograms effectively
• Compare hierarchical vs K-Means