smile.clustering

Class GMeans

java.lang.Object

smile.clustering.PartitionClustering<double[]>

smile.clustering.KMeans

smile.clustering.GMeans

All Implemented Interfaces:

java.io.Serializable, Clustering<double[]>

public class GMeans
extends KMeans
implements java.io.Serializable

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, Serialized Form

Field Summary

Fields inherited from class smile.clustering.PartitionClustering

k, size, y

Fields inherited from interface smile.clustering.Clustering

OUTLIER

Constructor Summary

Constructors

Constructor and Description
GMeans(double[][] data, int kmax) Constructor.

Method Summary

Modifier and Type	Method and Description
java.lang.String	toString()

Methods inherited from class smile.clustering.KMeans

centroids, distortion, lloyd, lloyd, lloyd, predict

Methods inherited from class smile.clustering.PartitionClustering

getClusterLabel, getClusterSize, getNumClusters, seed, seed

Methods inherited from class java.lang.Object

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

Constructor Detail

GMeans

public GMeans(double[][] data,
              int kmax)

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

Parameters:

data - the input data of which each row is a sample.

kmax - the maximum number of clusters.

Method Detail

toString

public java.lang.String toString()

Overrides:

toString in class KMeans