Unsupervised Learning Techniques

Introduction

Unsupervised learning is a type of machine learning where the model is trained on data without labeled responses. The goal is to find underlying patterns or structures in the data. It's widely used in clustering, dimensionality reduction, and association rule learning.

Common Unsupervised Learning Techniques

• K-means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Autoencoders

Code Example: K-means Clustering

Below is a simple implementation of K-means clustering using Python's scikit-learn library. This example demonstrates how to cluster data points into groups.

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

# Generate random data
data = np.random.rand(100, 2)

# Create KMeans model
kmeans = KMeans(n_clusters=3)

# Fit the model
kmeans.fit(data)

# Get cluster centers and labels
centers = kmeans.cluster_centers_
labels = kmeans.labels_

# Plot the results
plt.scatter(data[:, 0], data[:, 1], c=labels, cmap='viridis')
plt.scatter(centers[:, 0], centers[:, 1], s=300, c='red', label='Centroids')
plt.title('K-means Clustering')
plt.legend()
plt.show()
                

Step-by-Step Flowchart

graph TD;
    A[Start] --> B[Collect Data];
    B --> C[Preprocess Data];
    C --> D{Choose Algorithm};
    D -->|K-means| E[K-means Clustering];
    D -->|PCA| F[Principal Component Analysis];
    D -->|Hierarchical| G[Hierarchical Clustering];
    E --> H[Evaluate Clusters];
    F --> H;
    G --> H;
    H --> I[Visualize Results];
    I --> J[End];