CH_03_02: Types of ML :Unsupervised Learning

Unsupervised Learning

Definition:

Unsupervised learning is a machine learning technique where the model learns from unlabeled data, identifying patterns, structures, or relationships without predefined outputs.

Types of Unsupervised Learning

1. Clustering (Grouping Similar Data Points)

Purpose: Groups similar data points into clusters based on shared characteristics.

Example: Customer segmentation (grouping customers based on purchasing behavior).

Common Algorithms:

k-Means Clustering (divides data into k clusters)

Hierarchical Clustering (creates a tree-like structure of clusters)

DBSCAN (density-based clustering, finds noise and outliers)

2. Association Rule Learning (Finding Relationships Between Data Points)

Purpose: Identifies hidden relationships between variables.

Example: Market Basket Analysis (customers who buy milk often buy bread).

Common Algorithms:

Apriori Algorithm (generates frequent itemsets and association rules)

FP-Growth Algorithm (faster than Apriori, avoids candidate generation)

Eclat Algorithm (depth-first search for frequent itemsets)

3. Dimensionality Reduction (Reducing Feature Space)

Purpose: Reduces the number of variables while preserving essential information.

Example: Image compression (reducing image size while maintaining quality).

Common Algorithms:

Principal Component Analysis (PCA) (reduces data dimensions while maximizing variance)

t-SNE (used for data visualization, preserves local structure)

Singular Value Decomposition (SVD) (factorizes data matrices into simpler components)

4. Anomaly Detection (Finding Unusual Data Points)

Purpose: Identifies rare or unexpected patterns in data.

Example: Fraud detection in banking (detecting abnormal transactions).

Common Algorithms:

Isolation Forest (randomly isolates outliers)

k-Means for Outlier Detection (identifies distant points from clusters)

Autoencoders (deep learning-based anomaly detection)

Key Differences Between Supervised and Unsupervised Learning

Real-World Applications of Unsupervised Learning

Advantages of Unsupervised Learning

✅ Finds hidden patterns without human intervention

✅ Works well with large datasets

✅ Helps in feature engineering and preprocessing

✅ Reduces dimensionality for better performance

Disadvantages of Unsupervised Learning

❌ Results may not always be interpretable

❌ Clustering algorithms require manual tuning (choosing k in k-Means)

❌ No accuracy metrics like in supervised learning (hard to validate results)