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Data Science and Machine Learning Interview Handbook

Hierarchical Clustering

Explore hierarchical clustering fundamentals and how to implement it using efficient techniques. Understand how to visualize dendrograms, select appropriate distance metrics, and interpret clusters to uncover nested data structures in unsupervised learning.

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Hierarchical clustering is a powerful technique for discovering nested structures in data, often revealing hidden patterns that flat clustering methods can miss. In this lesson, we’ll build a hierarchical clustering workflow, visualize the results using dendrograms, and compare different distance metrics for clustering quality. Let’s get started.

Hierarchical clustering implementation

Hierarchical clustering is a popular unsupervised learning algorithm we use within our company. It helps us identify natural groupings within data, which can be crucial for uncovering hidden patterns and insights.

Implement a simple hierarchical clustering algorithm that performs linkage and creates a diagram, given sample data. Your implementation should be efficient, can leverage scipy, and needs to visualize the dendrogram for a sample dataset.

Python 3.10.4
import numpy as np
from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.datasets import make_blobs
def perform_hierarchical_clustering(X):
    # TODO: Implement hierarchical clustering
    # 1. Perform linkage 
    # 2. Create dendrogram
    # Hint: Use linkage() and dendrogram() from scipy.cluster.hierarchy
    
    # Your implementation here
    pass
# Generate sample data
X, _ = make_blobs(n_samples=50, centers=3, random_state=42)
# Call your function
perform_hierarchical_clustering(X)

Sample answer

Here’s how we can break this down:

  1. Prepare the data: Normalize features if they are on ...