Part 5: Building Your Own AI - Exploring Unsupervised Learning and Clustering

Author: Trix Cyrus

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Unsupervised learning offers powerful techniques for extracting insights from unlabeled data, making it essential for discovering hidden patterns and relationships. In this article, we’ll focus on clustering algorithms such as K-Means and hierarchical clustering and introduce dimensionality reduction techniques like Principal Component Analysis (PCA). Real-world applications, such as customer segmentation and anomaly detection, will demonstrate the practical utility of these methods.

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1. What Is Unsupervised Learning?

• Definition: Learning patterns from data without pre-existing labels.
• Objective: Group or structure data in meaningful ways, revealing intrinsic structures.
• Applications:
  • Market segmentation.
  • Fraud detection.
  • Recommendation systems.

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2. Clustering Algorithms

a. K-Means Clustering

• How It Works:
  1. Select the number of clusters ((k)).
  2. Randomly initialize cluster centroids.
  3. Assign data points to the nearest centroid.
  4. Recalculate centroids based on assignments.
  5. Repeat until convergence.
• Example Use Case: Grouping customers based on purchasing behavior.
• Advantages: Simple, fast, scalable.
• Limitations: Requires pre-defining (k); sensitive to outliers.

Code Example:

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import make_blobs

# Generate synthetic data
X, _ = make_blobs(n_samples=300, centers=4, random_state=42, cluster_std=1.0)

# Apply K-Means
kmeans = KMeans(n_clusters=4, random_state=42)
kmeans.fit(X)
labels = kmeans.labels_

# Visualize clusters
sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=labels, palette='viridis')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=200, c='red', label='Centroids')
plt.legend()
plt.show()

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b. Hierarchical Clustering

• How It Works:
  • Creates a tree-like structure (dendrogram) to represent data groupings.
  • Two approaches:
  • Agglomerative: Bottom-up, merging clusters.
  • Divisive: Top-down, splitting clusters.
• Example Use Case: Gene expression analysis in bioinformatics.
• Advantages: No need to pre-define the number of clusters.
• Limitations: Computationally expensive for large datasets.

Code Example:

from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt

# Generate synthetic data
X, _ = make_blobs(n_samples=150, centers=3, random_state=42, cluster_std=1.2)

# Apply hierarchical clustering
linked = linkage(X, method='ward')

# Plot dendrogram
plt.figure(figsize=(10, 7))
dendrogram(linked, truncate_mode='lastp', p=10, leaf_rotation=90, leaf_font_size=10)
plt.title('Hierarchical Clustering Dendrogram')
plt.show()

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3. Dimensionality Reduction

a. Principal Component Analysis (PCA)

• Purpose: Reduce the number of dimensions while retaining most of the data’s variability.
• How It Works:
  • Identifies principal components (orthogonal vectors) capturing maximum variance.
  • Projects data onto these components.
• Example Use Case: Visualizing high-dimensional data in 2D or 3D.
• Advantages: Reduces noise and improves computational efficiency.
• Limitations: May lose interpretability of original features.

Code Example:

from sklearn.decomposition import PCA
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt

# Load Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Apply PCA
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

# Plot PCA results
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap='viridis', edgecolor='k')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.title('PCA on Iris Dataset')
plt.show()

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4. Real-World Applications

a. Customer Segmentation

• Goal: Group customers based on behavior, demographics, or preferences.
• Approach:
  • Use K-Means to cluster purchase data.
  • Visualize clusters for insights.

b. Anomaly Detection

• Goal: Identify outliers or unusual patterns, such as fraudulent transactions.
• Approach:
  • Use clustering to find normal data patterns.
  • Points far from cluster centroids are flagged as anomalies.

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~Trixsec