# Clustering

`linfa-clustering` aims to provide pure Rust implementations of popular clustering algorithms.

## The big picture

`linfa-clustering` is a crate in the [`linfa`](https://crates.io/crates/linfa) ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python's `scikit-learn`.

You can find a roadmap (and a selection of good first issues)
[here](https://github.com/rust-ml/linfa/issues) - contributors are more than welcome!

## Current state

`linfa-clustering` currently provides implementation of the following clustering algorithms, in addition to a couple of helper functions: 
- K-Means
- DBSCAN
- Approximated DBSCAN (Currently an alias for DBSCAN, due to its superior performance)
- Gaussian Mixture Model


Implementation choices, algorithmic details and a tutorial can be found 
[here](https://docs.rs/linfa-clustering).

## BLAS/Lapack backend
We found that the pure Rust implementation maintained similar performance to the BLAS/LAPACK version and have removed it with this [PR](https://github.com/rust-ml/linfa/pull/257). Thus, to reduce code complexity BLAS support has been removed for this module.

## License
Dual-licensed to be compatible with the Rust project.

Licensed under the Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0 or the MIT license http://opensource.org/licenses/MIT, at your option. This file may not be copied, modified, or distributed except according to those terms.