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K-MC2

A fast Plus Plus approximator that replaces the brute force method with a substantially faster Markov Chain Monte Carlo (MCMC) sampling procedure with comparable results.

Parameters

#	Name	Default	Type	Description
1	m	50	int	The number of candidate nodes in the Markov Chain.
2	kernel	Euclidean	Distance	The distance kernel used to compute the distance between samples.

Example

use Rubix\ML\Clusterers\Seeders\KMC2;
use Rubix\ML\Kernels\Distance\Euclidean;

$seeder = new KMC2(200, new Euclidean());

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1. O. Bachem et al. (2016). Approximate K-Means++ in Sublinear Time. ↩