Clustering

linfa-hierarchical provides an implementation of agglomerative hierarchical clustering. In this clustering algorithm, each point is first considered as a separate cluster. During each step, two points are merged into new clusters, until a stopping criterion is reached. The distance between the points is computed as the negative-log transform of the similarity kernel.

Documentation: latest.

The big picture

linfa-hierarchical is a crate in the linfa ecosystem, a wider effort to bootstrap a toolkit for classical Machine Learning implemented in pure Rust, akin in spirit to Python's scikit-learn.

Current state

linfa-hierarchical implements agglomerative hierarchical clustering with support of the kodama crate.

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.