Unsupervised Learning

Machine learning that finds patterns in data without labeled examples or target outputs.

Unsupervised learning discovers hidden structures in data where no correct answers are provided. It identifies patterns, groups similar data points, or learns compressed representations without explicit supervision.

Examples: Customer segmentation through clustering, anomaly detection in network traffic, dimensionality reduction for data visualization, recommendation systems based on user behavior.

Challenges

Difficult to evaluate results without ground truth, determining optimal number of clusters or components, interpreting discovered patterns, and ensuring patterns are meaningful rather than artifacts.