Python Hierarchical Clustering

Last modified on: October 20, 2024

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<h3><strong>Hierarchical Clustering in Python</strong></h3>
<p><strong>Hierarchical Clustering</strong> is a method of cluster analysis that seeks to build a hierarchy of clusters. It can be used for both agglomerative (bottom-up) and divisive (top-down) clustering. Agglomerative clustering starts with individual points and merges them into clusters, while divisive clustering starts with one cluster and divides it into smaller ones.</p>
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<h3><strong>Key Concepts of Hierarchical Clustering</strong></h3>
<ul>
<li><strong>Dendrogram</strong>: A tree-like diagram that shows the arrangement of the clusters and the distances at which they merge.</li>
<li><strong>Linkage Criteria</strong>: The method used to calculate the distance between clusters. Common methods include:
<ul>
<li><strong>Single Linkage</strong>: Minimum distance between points in two clusters.</li>
<li><strong>Complete Linkage</strong>: Maximum distance between points in two clusters.</li>
<li><strong>Average Linkage</strong>: Average distance between points in two clusters.</li>
<li><strong>Ward's Method</strong>: Minimizes the total within-cluster variance.</li>
</ul>
</li>
</ul>
<h3><strong>Advantages of Hierarchical Clustering</strong></h3>
<ul>
<li>Does not require a predefined number of clusters.</li>
<li>Provides a comprehensive view of data relationships.</li>
</ul>
<h3><strong>Disadvantages of Hierarchical Clustering</strong></h3>
<ul>
<li>Computationally intensive, especially for large datasets.</li>
<li>Sensitive to noise and outliers.</li>
</ul>
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<h3><strong>Tutorial: Performing Hierarchical Clustering in Python</strong></h3>
<p>We will use Python&rsquo;s <code>scikit-learn</code> library to perform hierarchical clustering and visualize the results.</p>
<h3><strong>1. Import Required Libraries</strong></h3>
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<pre class="language-python"><code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_iris
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, linkage</code></pre>
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<h3><strong>2. Preparing the Data</strong></h3>
<p>For this example, we&rsquo;ll use the <strong>Iris dataset</strong>.</p>
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<pre class="language-python"><code># Load the Iris dataset
iris = load_iris()
X = iris.data  # Features
y = iris.target  # Target variable (not used in clustering)

# Convert to DataFrame for better visualization
df = pd.DataFrame(data=X, columns=iris.feature_names)
print(df.head())</code></pre>
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<h3><strong>3. Performing Agglomerative Clustering</strong></h3>
<p>We can perform agglomerative clustering using the <code>AgglomerativeClustering</code> class.</p>
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<pre class="language-python"><code># Initialize the model with the desired number of clusters
n_clusters = 3
model = AgglomerativeClustering(n_clusters=n_clusters)

# Fit the model
labels = model.fit_predict(X)

# Add cluster labels to the DataFrame
df['Cluster'] = labels</code></pre>
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<h3><strong>4. Visualizing the Dendrogram</strong></h3>
<p>To visualize how clusters are formed, we can create a dendrogram.</p>
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<pre class="language-python"><code># Compute the linkage matrix
Z = linkage(X, method='ward')

# Plot the dendrogram
plt.figure(figsize=(12, 6))
dendrogram(Z, labels=iris.target_names)
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('Species')
plt.ylabel('Distance')
plt.show()</code></pre>
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<h3><strong>5. Visualizing the Clusters</strong></h3>
<p>We can visualize the clusters formed by the hierarchical clustering.</p>
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<pre class="language-python"><code># Plot the clusters
plt.figure(figsize=(10, 6))
sns.scatterplot(x=df.iloc[:, 0], y=df.iloc[:, 1], hue='Cluster', palette='viridis', data=df, s=100)
plt.title('Hierarchical Clustering Results')
plt.xlabel(iris.feature_names[0])
plt.ylabel(iris.feature_names[1])
plt.show()</code></pre>
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<h3><strong>Conclusion</strong></h3>
<p>In this tutorial, we:</p>
<ol>
<li>Performed <strong>Hierarchical Clustering</strong> on the Iris dataset using agglomerative clustering.</li>
<li>Visualized the clustering process using a <strong>dendrogram</strong>.</li>
<li>Visualized the resulting clusters in a scatter plot.</li>
</ol>
<p>Hierarchical clustering is a powerful method for understanding the relationships between data points, providing valuable insights, especially when the number of clusters is unknown.</p>
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