Clustering

This crate provides an easy and efficient way to perform kmeans clustering on arbitrary data. The algo is initialized with kmeans++ for best performance of the clustering.

There are three goals to this implementation of the kmeans algorithm:

1. it must be generic
2. it must be easy to use
3. it must be reasonably fast

Important Note

Depending on your execution environment and the size of the dataset you aim to cluster; your code might benefit from parallelisation (this can mean massive performance improvements for large problems). Should you want to enable the multithreaded behavior, then add the "parallel" feature to your dependencies.

# To enable multithreading during clustering, add the "parallel" feature
# to your dependency.
[dependencies]
clustering = {version = "0.2.0", features = ["parallel"]}

# If all you aim for it a sequential clustering, just leave that feature out.
[dependencies]
clustering = {version = "0.2.0"}

Example

use clustering::*;

let n_samples    = 20_000; // # of samples in the example
let n_dimensions =    200; // # of dimensions in each sample
let k            =      4; // # of clusters in the result
let max_iter     =    100; // max number of iterations before the clustering forcefully stops

// Generate some random data
let mut samples: Vec<Vec<f64>> = vec![];
for _ in 0..n_samples {
    samples.push((0..n_dimensions).map(|_| rand::random()).collect::<Vec<_>>());
}

// actually perform the clustering
let clustering = kmeans(k, &samples, max_iter);

println!("membership: {:?}", clustering.membership);
println!("centroids : {:?}", clustering.centroids);

Features

This crate comes with two optional features:

• parallel which enables multithreaded dispatch with rayon (thanks to @jean-pierreBoth 's contribution)
• logging which you can use to log when clustering takes shortcuts.