Clustering
An unsupervised learning technique that groups similar data points together without predefined labels.
How It Works
Clustering algorithms find natural groupings in data by measuring similarity between data points. Points within a cluster are more similar to each other than to points in other clusters.
Common Algorithms
K-Means: Partitions data into K clusters by minimizing within-cluster distances. Fast but requires specifying K. DBSCAN: Density-based clustering that can find arbitrary-shaped clusters and identify outliers. Hierarchical: Builds a tree of clusters through successive merging or splitting.
Applications
Customer segmentation, document grouping, image segmentation, gene expression analysis, and anomaly detection. Combined with embeddings, clustering can discover topics in large text corpora.