Definition
Unsupervised Learning
Unsupervised learning finds patterns, groups or structure in data without using pre-labeled answers.
Short definition
Unsupervised learning is a machine learning approach where the model receives data without explicit labels. It tries to discover structure, similarity, clusters or compressed representations on its own.
How it works
The model may group similar records together, reduce high-dimensional data into a smaller representation or find unusual points that do not fit common patterns. Unlike supervised learning, there is no single correct answer for each example.
Example
An ecommerce company can cluster customers based on browsing and purchasing behavior. The resulting groups can help teams understand segments even if no one labeled those segments in advance.
Why it matters
Unsupervised learning is useful when labeled data is limited or expensive. It is common in anomaly detection, customer segmentation, embeddings and exploratory analysis. Results still need interpretation because discovered patterns are not automatically meaningful.