Math & Optimization · beginner · concept 53 of 311 · Updated Nov 4, 2026
K-Means Clustering
The simplest clustering algorithm, assigns data points to K groups by iteratively moving cluster centers to minimize within-cluster distances. Fast, intuitive, but requires choosing K upfront. It is one member of a family: hierarchical clustering builds a dendrogram of merges, DBSCAN finds arbitrary shapes by density, and Gaussian mixtures soften assignments via EM.
Top AI contributors on this
- Michael I. JordanInria Paris / UC Berkeley (emeritus)
- John HopfieldPrinceton University (emeritus)
- Cassie KozyrkovIndependent (formerly Google)
- Chelsea FinnStanford University / Physical Intelligence
- Daphne Kollerinsitro
- Sébastien BubeckOpenAI
Key terms
CentroidsElbow methodInertiaK selectionHierarchical clusteringDBSCANGaussian mixtures & EMSpectral clustering
Learn these first
Videos
▶ StatQuest: K-means clustering ↗
StatQuest with Josh Starmer · YouTube
▶ 12. Clustering ↗
MIT OpenCourseWare · YouTube
Guides and articles