Crate 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

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);

Structs

Centroid

A centroid: a collection of n abstract quantities (which must be interpreted in the context of what you are doing).

Clustering

This is the result of a kmeans clustering

Traits

Elem

This is the trait you will want to implement for the types you wish to cluster.

Functions

kmeans

This function returns a clustering that groups the given set of ‘elems’ in ‘k’ clusters and will at most perform ‘iter’ iterations before stopping