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Fuzzy C Means#

A distance-based soft-clustering algorithm that allows samples to belong to multiple clusters if they fall within a fuzzy region controlled by the fuzz hyper-parameter. Like K Means, Fuzzy C Means minimizes the inertia cost function, however, unlike K Means, FCM uses a batch solver that requires the entire training set to compute the update to the cluster centroids at each step.

Interfaces: Estimator, Learner, Probabilistic, Verbose, Persistable

Data Type Compatibility: Continuous


# Name Default Type Description
1 c int The number of target clusters.
2 fuzz 2.0 float Determines the bandwidth of the fuzzy area.
3 epochs 300 int The maximum number of training rounds to execute.
4 minChange 1e-4 float The minimum change in the inertia for the algorithm to continue training.
5 kernel Euclidean Distance The distance kernel used to compute the distance between sample points.
6 seeder PlusPlus Seeder The seeder used to initialize the cluster centroids.


use Rubix\ML\Clusterers\FuzzyCMeans;
use Rubix\ML\Kernels\Distance\Euclidean;
use Rubix\ML\Clusterers\Seeders\Random;

$estimator = new FuzzyCMeans(5, 1.2, 400, 1., new Euclidean(), new Random());

Additional Methods#

Return the c computed centroids of the training set:

public centroids() : array[]

Return an iterable progress table with the steps from the last training session:

public steps() : iterable

use Rubix\ML\Extractors\CSV;

$extractor = new CSV('progress.csv', true);


Returns the inertia at each epoch from the last round of training:

public losses() : float[]|null


  1. J. C. Bezdek et al. (1984). FCM: The Fuzzy C-Means Clustering Algorithm.