Package elki.clustering.kmeans

Class YinYangKMeans.Instance

java.lang.Object

elki.clustering.kmeans.AbstractKMeans.Instance

elki.clustering.kmeans.YinYangKMeans.Instance

Enclosing class:

YinYangKMeans<V extends NumberVector>

protected static class YinYangKMeans.Instance
extends AbstractKMeans.Instance

Instance for a particular data set.

Author:

Minh Nhat Nguyen, Erich Schubert

Field Summary

Fields

Modifier and Type	Field	Description
(package private) double[]	cdrift	Distance moved by each center.
(package private) double[]	gdrift	Maximum distance moved within each group.
(package private) int[]	glabel	Group label of each mean
(package private) int[][]	groups	Center list for each group
(package private) WritableDataStore<double[]>	lower	Lower bounds
(package private) double[][]	sums	Current cluster sum.
(package private) WritableDoubleDataStore	upper	Upper bound

Fields inherited from class elki.clustering.kmeans.AbstractKMeans.Instance

assignment, clusters, diststat, isSquared, k, key, means, relation, varsum

Constructor Summary

Constructors

Constructor	Description
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means, int t)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected int	assignToNearestCluster()	Reassign objects, but avoid unnecessary computations based on their bounds.
protected Logging	getLogger()	Get the class logger.
private int[][]	groupKMeans(int t)	Groups the initial centers into t groups.
private int	initialAssignToNearestCluster()	Perform initial cluster assignment,
private void	initialGroupAssignment(int t, double[][] scratch, int[] gweight)	Initial k-means assignment for centers to groups.
protected int	iterate(int iteration)	Main loop function.
void	run(int maxiter)	Run the clustering.
private void	updateCenters()	Update centers and how much they moved.
private boolean	updateGroupAssignment(int t, double[][] gmeans, int[] gweight)	Perform one step of Voronoi refinement.

Methods inherited from class elki.clustering.kmeans.AbstractKMeans.Instance

buildResult, buildResult, computeSquaredSeparation, copyMeans, distance, distance, distance, initialSeperation, meansFromSums, movedDistance, recomputeSeperation, recomputeVariance, sqrtdistance, sqrtdistance, sqrtdistance

Methods inherited from class java.lang.Object

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

Field Detail

groups

int[][] groups

Center list for each group

gdrift

double[] gdrift

Maximum distance moved within each group.

cdrift

double[] cdrift

Distance moved by each center.

sums

double[][] sums

Current cluster sum.

glabel

int[] glabel

Group label of each mean

upper

WritableDoubleDataStore upper

Upper bound

lower

WritableDataStore<double[]> lower

Lower bounds

Constructor Detail

Instance

public Instance(Relation<? extends NumberVector> relation,
                NumberVectorDistance<?> df,
                double[][] means,
                int t)

Constructor.

Parameters:

relation - Data relation

df - Distance function

means - Initial means

t - Number of groups to use

Method Detail

run

public void run(int maxiter)

Description copied from class: AbstractKMeans.Instance

Run the clustering.

Overrides:

run in class AbstractKMeans.Instance

Parameters:

maxiter - Maximum number of iterations

groupKMeans

private int[][] groupKMeans(int t)

Groups the initial centers into t groups.

Parameters:

t - Number of groups

Returns:

a list of groups containing mean indices.

initialGroupAssignment

private void initialGroupAssignment(int t,
                                    double[][] scratch,
                                    int[] gweight)

Initial k-means assignment for centers to groups.

Parameters:

t - Number of groups

scratch - Scratch space for means

gweight - group weights

updateGroupAssignment

private boolean updateGroupAssignment(int t,
                                      double[][] gmeans,
                                      int[] gweight)

Perform one step of Voronoi refinement.

Parameters:

t - Number of groups

gmeans - current group means

gweight - Group weight

Returns:

true if changed

iterate

protected int iterate(int iteration)

Description copied from class: AbstractKMeans.Instance

Main loop function.

Specified by:

iterate in class AbstractKMeans.Instance

Parameters:

iteration - Iteration number (beginning at 1)

Returns:

Number of reassigned points

updateCenters

private void updateCenters()

Update centers and how much they moved.

assignToNearestCluster

protected int assignToNearestCluster()

Reassign objects, but avoid unnecessary computations based on their bounds.

Overrides:

assignToNearestCluster in class AbstractKMeans.Instance

Returns:

number of objects reassigned

initialAssignToNearestCluster

private int initialAssignToNearestCluster()

Perform initial cluster assignment,

Returns:

number of changes (i.e. relation size)

getLogger

protected Logging getLogger()

Description copied from class: AbstractKMeans.Instance

Get the class logger.

Specified by:

getLogger in class AbstractKMeans.Instance

Returns:

Logger