Unsupervised Learning is a Machine Learning technique where models learn patterns without labeled data.
The system is not told what the correct output is—it must discover structure on its own.
This makes it useful for exploring data, grouping items, detecting anomalies, and finding hidden relationships.
How Unsupervised Learning Works
- You provide unlabeled data (no categories or target values).
- The algorithm analyzes the data.
- It identifies patterns, clusters, or structures.
- The model groups or organizes data based on similarities.
It’s like giving a child a box of mixed toys and letting them sort them without instructions.
Main Types of Unsupervised Learning
1. Clustering
Groups similar data points together.
Examples:
- Customer segmentation
- Grouping similar images
- Document clustering
2. Association
Finds relationships between items.
Examples:
- “People who buy X also buy Y”
- Market basket analysis
Popular Unsupervised Learning Algorithms
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Principal Component Analysis (PCA)
- Apriori Algorithm
Real-World Applications
- Grouping customers for targeted advertising
- Organizing large document collections
- Detecting fraud or unusual patterns
- Recommending similar products
- Reducing dimensions of large datasets
Unsupervised learning is especially useful when you have data but no labels.
Conclusion
Unsupervised learning helps machines discover hidden structures in data without any guidance. It is essential for data exploration, clustering, and pattern detection tasks.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/