Hierarchical Clustering Implementation
├── Introduction
│   └── Overview of Hierarchical Clustering
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Generating the Dataset
├── Implementing Hierarchical Clustering
│   ├── Data Preparation
│   ├── Model Training
│   └── Creating Dendrogram
├── Visualization
│   └── Hierarchical Clustering Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of Hierarchical Clustering

• Hierarchical Clustering is an algorithm that builds a hierarchy of clusters. It can be implemented using either a bottom-up (agglomerative) or a top-down (divisive) approach.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.datasets import make_blobs

Generating the Dataset

# Python code to generate a sample dataset
X, _ = make_blobs(n_samples=150, centers=4, cluster_std=1.2, random_state=42)

3. Implementing Hierarchical Clustering

Data Preparation

• No target variable is needed as hierarchical clustering is an unsupervised algorithm.

Model Training

# Python code to perform hierarchical clustering
linked = linkage(X, method='ward')

Creating Dendrogram

# Python code to create a dendrogram
dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True)
plt.title('Hierarchical Clustering Dendrogram')
plt.show()

4. Visualization

Hierarchical Clustering Visualization