Clustering and latent structure · MLD-0592
K-means
Clustering by alternating point-to-center assignments and centroid updates to reduce squared distances.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Group embeddings into k clusters.
Original caveat
It favors roughly spherical, similarly scaled clusters.
Context and aliases
Level: Intermediate. Stages: Training · Data · Evaluation.
Tags: ML · DL.
Aliases: None recorded.
Related concepts
Concepts linking here
Original references
- scikit-learn: clustering ↗ · Reference link
- scikit-learn: matrix decomposition ↗ · Reference link
- scikit-learn: manifold learning ↗ · Reference link
- scikit-learn: novelty and outlier detection ↗ · Reference link
- Original reference URL ↗