smile.clustering

Class DeterministicAnnealing

java.lang.Object

smile.clustering.PartitionClustering<double[]>

smile.clustering.KMeans

smile.clustering.DeterministicAnnealing

All Implemented Interfaces:

Clustering<double[]>

public class DeterministicAnnealing
extends KMeans

Deterministic annealing clustering. Deterministic annealing extends soft-clustering to an annealing process. For each temperature value, the algorithm iterates between the calculation of all posteriori probabilities and the update of the centroids vectors, until convergence is reached. The annealing starts with a high temperature. Here, all centroids vectors converge to the center of the pattern distribution (independent of their initial positions). Below a critical temperature the vectors start to split. Further decreasing the temperature leads to more splittings until all centroids vectors are separate. The annealing can therefore avoid (if it is sufficiently slow) the convergence to local minima.

References

1. Kenneth Rose. Deterministic Annealing for Clustering, Compression, Classification, Regression, and Speech Recognition.

Author:

Haifeng Li

Field Summary

Fields inherited from interface smile.clustering.Clustering

OUTLIER

Constructor Summary

Constructors

Constructor and Description
DeterministicAnnealing(double[][] data, int Kmax) Constructor.
DeterministicAnnealing(double[][] data, int Kmax, double alpha) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
double	getAlpha() Returns the annealing parameter.
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

Methods inherited from class java.lang.Object

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

Constructor Detail

DeterministicAnnealing

public DeterministicAnnealing(double[][] data,
                      int Kmax)

Constructor. Clustering data into k clusters.

Parameters:

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

Kmax - the maximum number of clusters.

DeterministicAnnealing

public DeterministicAnnealing(double[][] data,
                      int Kmax,
                      double alpha)

Constructor. Clustering data into k clusters.

Parameters:

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

Kmax - the maximum number of clusters.

alpha - the temperature T is decreasing as T = T * alpha. alpha has to be in (0, 1).

Method Detail

getAlpha

public double getAlpha()

Returns the annealing parameter.

Returns:

the annealing parameter

toString

public String toString()

Overrides:

toString in class KMeans