Package smile.clustering

Class XMeans

java.lang.Object

smile.clustering.PartitionClustering

smile.clustering.CentroidClustering<double[],double[]>

smile.clustering.XMeans

All Implemented Interfaces:

Serializable, Comparable<CentroidClustering<double[],double[]>>

public class XMeans
extends CentroidClustering<double[],double[]>

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 class smile.clustering.CentroidClustering

centroids, distortion

Fields inherited from class smile.clustering.PartitionClustering

k, OUTLIER, size, y

Constructor Summary

Constructors

Constructor	Description
XMeans(double distortion, double[][] centroids, int[] y)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected double	distance(double[] x, double[] y)	The distance function.
static XMeans	fit(double[][] data, int kmax)	Clustering data with the number of clusters determined by X-Means algorithm automatically.
static XMeans	fit(double[][] data, int kmax, int maxIter, double tol)	Clustering data with the number of clusters determined by X-Means algorithm automatically.

Methods inherited from class smile.clustering.CentroidClustering

compareTo, predict, toString

Methods inherited from class smile.clustering.PartitionClustering

run, seed

Methods inherited from class java.lang.Object

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

Constructor Details

XMeans

public XMeans(double distortion,
 double[][] centroids,
 int[] y)

Constructor.

Parameters:

distortion - the total distortion.

centroids - the centroids of each cluster.

y - the cluster labels.

Method Details

distance

protected double distance(double[] x,
 double[] y)

Description copied from class: CentroidClustering

The distance function.

Specified by:

distance in class CentroidClustering<double[],double[]>

Parameters:

x - an observation.

y - the other observation.

Returns:

the distance.

fit

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

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

Parameters:

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

kmax - the maximum number of clusters.

Returns:

the model.

fit

public static XMeans fit(double[][] data,
 int kmax,
 int maxIter,
 double tol)

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

tol - the tolerance of k-means convergence test.

Returns:

the model.