linfa-clustering aims to provide pure Rust implementations of popular clustering algorithms.
linfa-clustering is a crate in the 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 - contributors are more than welcome!
linfa-clustering currently provides implementation of the following clustering algorithms, in addition to a couple of helper functions:
- K-Means
- DBSCAN
- Approximated DBSCAN
- Gaussian Mixture Model
Implementation choices, algorithmic details and a tutorial can be found here.
WARNING: Currently the Approximated DBSCAN implementation is slower than the normal DBSCAN implementation. Therefore DBSCAN should always be used over Approximated DBSCAN.
We found that the pure Rust implementation maintained similar performance to the BLAS/LAPACK version and have removed it with this PR. Thus, to reduce code complexity BLAS support has been removed for this module.
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.