Advanced Unsupervised Learning

1. Introduction

Unsupervised learning refers to the process of training a machine learning model on data without labeled responses. This lesson covers advanced techniques in unsupervised learning, focusing on clustering, dimensionality reduction, and anomaly detection.

2. Clustering Techniques

Clustering is the process of grouping data points into clusters based on similarity. Here are some advanced clustering algorithms:

2.1. K-Means Clustering

K-Means is a popular clustering algorithm that partitions data into K clusters. It iteratively refines cluster centroids to minimize the variance within clusters.


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

# Sample Data
X = np.random.rand(100, 2)

# K-Means Clustering
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
y_kmeans = kmeans.predict(X)

# Plotting
plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], c='red', s=200, alpha=0.75)
plt.title('K-Means Clustering')
plt.show()

2.2. DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is effective for spatial data and identifies clusters of varying density.


from sklearn.cluster import DBSCAN

# DBSCAN Clustering
dbscan = DBSCAN(eps=0.5, min_samples=5)
y_dbscan = dbscan.fit_predict(X)

# Plotting
plt.scatter(X[:, 0], X[:, 1], c=y_dbscan, s=50, cmap='plasma')
plt.title('DBSCAN Clustering')
plt.show()

3. Dimensionality Reduction

Dimensionality reduction techniques simplify data while retaining essential features. Key methods include:

Principal Component Analysis (PCA)

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Uniform Manifold Approximation and Projection (UMAP)

3.1. PCA Example


from sklearn.decomposition import PCA

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

# Plotting
plt.scatter(X_pca[:, 0], X_pca[:, 1], cmap='viridis')
plt.title('PCA Result')
plt.show()

4. Anomaly Detection

Anomaly detection identifies data points that deviate significantly from the majority of the data. Techniques include:

Isolation Forest

One-Class SVM

Autoencoders

5. Best Practices

When implementing unsupervised learning, consider the following best practices:

Preprocess your data thoroughly.

Experiment with different algorithms to find the best fit.

Use evaluation metrics such as silhouette score for clustering.

Visualize your data before and after applying algorithms.

6. FAQ

What is the main difference between supervised and unsupervised learning?

Supervised learning uses labeled data to train models, while unsupervised learning works with unlabeled data to uncover patterns.

Can unsupervised learning be used for classification tasks?

While unsupervised learning is primarily used for clustering and pattern discovery, the insights gained can sometimes inform classification tasks.

What are some common applications of unsupervised learning?

Common applications include customer segmentation, anomaly detection in network security, and image compression.