[source] # 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](k-means.md), 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](../estimator.md), [Learner](../learner.md), [Probabilistic](../probabilistic.md), [Verbose](../verbose.md), [Persistable](../persistable.md) **Data Type Compatibility:** Continuous ## Parameters | # | 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. | ## Example ```php 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: ```php public centroids() : array[] ``` Return an iterable progress table with the steps from the last training session: ```php public steps() : iterable ``` ```php use Rubix\ML\Extractors\CSV; $extractor = new CSV('progress.csv', true); $extractor->export($estimator->steps()); ``` Returns the inertia at each epoch from the last round of training: ```php public losses() : float[]|null ``` ## References [^1]: J. C. Bezdek et al. (1984). FCM: The Fuzzy C-Means Clustering Algorithm.