Clustering and latent structure · MLD-0276
DBSCAN
A clustering method connecting sufficiently dense neighborhoods while labeling some points as noise.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Find non-spherical clusters and outliers.
Original caveat
One density scale can struggle with variable-density clusters.
Context and aliases
Level: Intermediate. Stages: Training · Data · Evaluation.
Tags: ML · DL.
Aliases: Density-based spatial clustering of applications with noise.
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 ↗