Overview
In unsupervised learning, the system is not told the 'right answer.' Instead, it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Key Concepts
- Clustering: Grouping similar data points together (e.g., K-Means).
- Association: Finding rules that describe your data (e.g., people who buy X also buy Y).
- Dimensionality Reduction: Reducing the number of variables under consideration (e.g., PCA).
Applications
- Customer segmentation.
- Anomaly detection.
- Data compression.