Clustering

Clustering is an unsupervised machine learning technique that partitions data into groups (clusters) where points within a cluster are more similar to each other than to points in other clusters. Unlike classification, clustering does not use predefined labels. It discovers structure in data automatically, making it useful for exploration, segmentation, and pattern discovery.

The most common clustering algorithms include K-Means (which partitions data into K clusters by minimizing within-cluster variance), DBSCAN (which finds clusters of arbitrary shape based on density), and hierarchical clustering (which builds a tree of nested clusters). Each has tradeoffs: K-Means is fast but requires specifying K upfront and assumes roughly spherical clusters. DBSCAN handles irregular shapes and identifies outliers but is sensitive to its density parameters. Hierarchical methods provide rich visualizations through dendrograms but scale poorly to large datasets.

In AI and NLP applications, clustering is used extensively. Document clustering groups similar texts for topic discovery. Embedding clustering identifies semantic categories in vector spaces. Customer segmentation groups users by behavior for recommendation systems. In retrieval-augmented generation pipelines, clustering helps organize document collections and can improve retrieval by routing queries to relevant clusters before performing fine-grained search. Clustering also plays a role in model training, from data deduplication to curriculum learning strategies that organize training examples by difficulty.