Overview
Cluster analysis (or clustering) is an unsupervised learning technique used to discover natural groupings in data. It is widely used for exploratory data analysis and pattern recognition.
Common Algorithms
- K-Means: Partitioning data into K distinct clusters based on distance to a centroid.
- Hierarchical Clustering: Building a tree-like structure of clusters.
- DBSCAN: Grouping points based on density.
Use Cases
- Market segmentation (grouping customers with similar behaviors).
- Image compression.
- Document clustering for topic discovery.