smile.clustering

Class XMeans

java.lang.Object

smile.clustering.PartitionClustering<double[]>

smile.clustering.KMeans

smile.clustering.XMeans

All Implemented Interfaces:

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

public class XMeans
extends KMeans
implements java.io.Serializable

X-Means clustering algorithm, an extended K-Means which tries to automatically determine the number of clusters based on BIC scores. Starting with only one cluster, the X-Means algorithm goes into action after each run of K-Means, making local decisions about which subset of the current centroids should split themselves in order to better fit the data. The splitting decision is done by computing the Bayesian Information Criterion (BIC).

References

1. Dan Pelleg and Andrew Moore. X-means: Extending K-means with Efficient Estimation of the Number of Clusters. ICML, 2000.

See Also:

KMeans, GMeans, Serialized Form

Field Summary

Fields inherited from interface smile.clustering.Clustering

OUTLIER

Constructor Summary

Constructors

Constructor and Description
XMeans(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

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

XMeans

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

Constructor. Clustering data with the number of clusters being automatically determined by X-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