Package smile.clustering

Class DeterministicAnnealing

java.lang.Object

smile.clustering.DeterministicAnnealing

public class DeterministicAnnealing
extends Object

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.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static final record	DeterministicAnnealing.Options	Deterministic annealing hyperparameters.

Method Summary

Modifier and Type	Method	Description
static CentroidClustering<double[],double[]>	fit(double[][] data, int kmax, double alpha, int maxIter)	Clustering data into k clusters.
static CentroidClustering<double[],double[]>	fit(double[][] data, DeterministicAnnealing.Options options)	Clustering data into k clusters.

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,
 double alpha,
 int maxIter)

Clustering data into k clusters.

Parameters:

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

kmax - the maximum number of clusters.

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

maxIter - the maximum number of iterations at each temperature.

Returns:

the model.

fit

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

Clustering data into k clusters.

Parameters:

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

options - the hyperparameters.

Returns:

the model.