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());
#
-
O. Bachem et al. (2016). Approximate K-Means++ in Sublinear Time. ↩
Last update:
2021-01-25