frjnn/bhtsne

Parallel Barnes-Hut t-SNE implementation written in Rust.

44
/ 100
Emerging

This project provides an accelerated way to visually explore complex, high-dimensional data. It takes your raw data (like measurements from an experiment or survey responses) and transforms it into a 2D or 3D map where similar data points are clustered together. This helps researchers, data scientists, or analysts discover hidden patterns and relationships in large datasets that are otherwise hard to see.

89 stars and 1,629 monthly downloads. No commits in the last 6 months.

Use this if you need to reduce the dimensionality of complex data for visualization and exploratory analysis, especially when dealing with large datasets where performance is critical.

Not ideal if you need a specific statistical model for inference or if interpretability of individual dimensions in the output is more important than overall data structure visualization.

data-visualization exploratory-data-analysis machine-learning-engineering pattern-recognition scientific-research
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 16 / 25
Maturity 16 / 25
Community 10 / 25

How are scores calculated?

Stars

89

Forks

7

Language

Rust

License

MIT

Last pushed

Jul 11, 2025

Monthly downloads

1,629

Commits (30d)

0

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