Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. It's used to find patterns and relationships in data without being explicitly told what to look for. This is particularly useful in scenarios where the data doesn't have labels or when the labels are not available.
Common Types of Unsupervised Learning
- Clustering: Grouping data into clusters based on similarity. (For more information, check out Clustering Algorithms).
- Association: Finding interesting relationships between variables in large databases. (Learn more about Association Rules).
- Dimensionality Reduction: Reducing the number of variables in a dataset while retaining the original data's structure. (Explore Dimensionality Reduction Techniques).
Use Cases
- Market Basket Analysis
- Image Compression
- Anomaly Detection
Clustering Example
Resources
For further reading, don't miss out on our Machine Learning Basics.