lmcinnes/umap

Health v2 · maintenance only

Adoption not part of the score

8281 stars · 879 forks observed · 2026-10-21

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

UMAP is a Python library implementing Uniform Manifold Approximation and Projection, a non-linear dimensionality reduction technique usable for visualization and general embedding. It models data with a fuzzy topological structure and finds low-dimensional projections that preserve it, serving as a faster alternative to t-SNE.

Use cases

• visualize high-dimensional data in 2D or 3D
• reduce dimensions before clustering
• find a faster t-SNE alternative for large datasets
• embed single-cell gene expression data
• transform new data points into an existing embedding
• preprocess features for machine learning pipelines

When to choose

• you need fast, scalable non-linear dimension reduction
• you want cluster-preserving 2D/3D visualizations of high-dimensional data
• you need to embed new data into an existing embedding or use inverse transforms

When to avoid

• you need strictly linear dimensionality reduction like PCA
• you require exact preservation of global distances rather than local structure
• you need a pure interpretable statistical model rather than an embedding

Facets

library · maturity stable

machine-learning data-visualization data-science machine-learning data-science data-visualization python windows dimensionality-reduction umap topological-data-analysis manifold-learning embedding tsne-alternative linux macos

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markdown · JSON · MCP: product_card(name="lmcinnes/umap")