Python K-means

Last modified on: October 19, 2024

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<h2 class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><strong>K-Means Clustering in Python</strong></h2>
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<p><strong>K-Means</strong> is a popular unsupervised machine learning algorithm used for clustering. It partitions the data into <span class="katex"><span class="katex-mathml">KK</span><span class="katex-html" aria-hidden="true"><span class="base"><span class="mord mathnormal">K</span></span></span></span> distinct clusters based on feature similarity. The goal is to minimize the variance within each cluster while maximizing the variance between clusters.</p>
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<h3><strong>Key Concepts of K-Means Clustering</strong></h3>
<ul>
<li><strong>Centroids</strong>: The center of each cluster, calculated as the mean of all points assigned to that cluster.</li>
<li><strong>Iterations</strong>: The algorithm iterates between assigning data points to the nearest centroid and updating the centroid positions until convergence (no changes in assignments).</li>
</ul>
<h3><strong>Advantages of K-Means</strong></h3>
<ul>
<li>Simple to implement and interpret.</li>
<li>Scales well with large datasets.</li>
</ul>
<h3><strong>Disadvantages of K-Means</strong></h3>
<ul>
<li>Requires the number of clusters <span class="katex"><span class="katex-mathml">KK</span><span class="katex-html" aria-hidden="true"><span class="base"><span class="mord mathnormal">K</span></span></span></span> to be specified beforehand.</li>
<li>Sensitive to outliers and noise.</li>
<li>Can converge to a local minimum.</li>
</ul>
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<h3><strong>Tutorial: Implementing K-Means Clustering in Python</strong></h3>
<p>We will use Python&rsquo;s <code>scikit-learn</code> library to implement K-Means clustering on a sample dataset.</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
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score</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 for 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. Choosing the Number of Clusters (K)</strong></h3>
<p>One common method to determine the optimal <span class="katex"><span class="katex-mathml">KK</span><span class="katex-html" aria-hidden="true"><span class="base"><span class="mord mathnormal">K</span></span></span></span> is the <strong>Elbow Method</strong>. You plot the sum of squared distances (inertia) for different values of <span class="katex"><span class="katex-mathml">KK</span><span class="katex-html" aria-hidden="true"><span class="base"><span class="mord mathnormal">K</span></span></span></span> and look for an "elbow" point.</p>
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<pre class="language-python"><code># Calculate inertia for different values of K
inertia = []
k_values = range(1, 11)

for k in k_values:
    kmeans = KMeans(n_clusters=k, random_state=42)
    kmeans.fit(X)
    inertia.append(kmeans.inertia_)

# Plotting the Elbow Curve
plt.figure(figsize=(10, 6))
plt.plot(k_values, inertia, marker='o')
plt.title('Elbow Method for Optimal K')
plt.xlabel('Number of Clusters (K)')
plt.ylabel('Inertia')
plt.show()</code></pre>
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<h3><strong>4. Performing K-Means Clustering</strong></h3>
<p>Now, we can perform K-Means clustering using the optimal number of clusters determined from the Elbow Method.</p>
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<pre class="language-python"><code># Initialize K-Means with the chosen number of clusters
optimal_k = 3  # Based on the Elbow Method
kmeans = KMeans(n_clusters=optimal_k, random_state=42)

# Fit the model
kmeans.fit(X)

# Get the cluster labels
labels = kmeans.labels_
df['Cluster'] = labels</code></pre>
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<h3><strong>5. Visualizing the Clusters</strong></h3>
<p>We can visualize the clusters formed by K-Means using a scatter plot.</p>
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<pre class="language-python"><code># Plotting the clusters
plt.figure(figsize=(10, 6))
plt.scatter(df.iloc[:, 0], df.iloc[:, 1], c=df['Cluster'], cmap='viridis', marker='o', edgecolor='k')
plt.title('K-Means Clustering Results')
plt.xlabel(iris.feature_names[0])
plt.ylabel(iris.feature_names[1])
plt.show()</code></pre>
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<h3><strong>6. Evaluating the Clustering</strong></h3>
<p>We can evaluate the quality of the clusters using the <strong>Silhouette Score</strong>.</p>
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<pre class="language-python"><code># Calculate the silhouette score
silhouette_avg = silhouette_score(X, labels)
print(f'Silhouette Score: {silhouette_avg:.2f}')</code></pre>
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<h3><strong>Conclusion</strong></h3>
<p>In this tutorial, we:</p>
<ol>
<li>Introduced <strong>K-Means Clustering</strong> as an unsupervised learning technique.</li>
<li>Used the <strong>Iris dataset</strong> to demonstrate how to implement K-Means clustering.</li>
<li>Employed the <strong>Elbow Method</strong> to determine the optimal number of clusters.</li>
<li>Visualized the clustering results and evaluated the model using the Silhouette Score.</li>
</ol>
<p>K-Means is a powerful clustering algorithm, particularly useful for partitioning datasets into distinct groups based on feature similarities. Understanding how to choose <span class="katex"><span class="katex-mathml">KK</span><span class="katex-html" aria-hidden="true"><span class="base"><span class="mord mathnormal">K</span></span></span></span> and evaluate the results is essential for effective clustering.</p>
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