Package smile.clustering

Class GMeans

java.lang.Object

smile.clustering.GMeans

public class GMeans
extends Object

G-Means clustering algorithm, an extended K-Means which tries to automatically determine the number of clusters by normality test. The G-means algorithm is based on a statistical test for the hypothesis that a subset of data follows a Gaussian distribution. G-means runs k-means with increasing k in a hierarchical fashion until the test accepts the hypothesis that the data assigned to each k-means center are Gaussian.

References

1. G. Hamerly and C. Elkan. Learning the k in k-means. NIPS, 2003.

See Also:

• KMeans
• XMeans

Method Summary

Modifier and Type	Method	Description
static CentroidClustering<double[],double[]>	fit(double[][] data, int kmax, int maxIter)	Clustering data with the number of clusters determined by G-Means algorithm automatically.
static CentroidClustering<double[],double[]>	fit(double[][] data, Clustering.Options options)	Clustering data with the number of clusters determined by G-Means algorithm automatically.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Details

fit

public static CentroidClustering<double[],double[]> fit(double[][] data,
 int kmax,
 int maxIter)

Clustering data with the number of clusters determined by G-Means algorithm automatically.

Parameters:

data - the input data of which each row is an observation.

kmax - the maximum number of clusters.

maxIter - the maximum number of iterations for k-means.

Returns:

the model.

fit

public static CentroidClustering<double[],double[]> fit(double[][] data,
 Clustering.Options options)

Clustering data with the number of clusters determined by G-Means algorithm automatically.

Parameters:

data - the input data of which each row is an observation.

options - the hyperparameters.

Returns:

the model.