de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Class KMeansSimplifiedElkan.Instance

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Instance

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansSimplifiedElkan.Instance

Direct Known Subclasses:

KMeansElkan.Instance

Enclosing class:

KMeansSimplifiedElkan<V extends NumberVector>

protected static class KMeansSimplifiedElkan.Instance
extends AbstractKMeans.Instance

Inner instance, storing state for a single data set.

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
(package private) WritableDataStore<double[]>	lower Lower bounds
(package private) double[][]	newmeans Scratch space for new means.
(package private) double[]	sep Cluster separation
(package private) double[][]	sums Sums of clusters.
(package private) WritableDoubleDataStore	upper Upper bounds

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Instance

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

Constructor Summary

Constructors

Constructor and Description
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.

Method Summary

Modifier and Type	Method and Description
protected int	assignToNearestCluster() Reassign objects, but avoid unnecessary computations based on their bounds.
protected Logging	getLogger() Get the class logger.
protected int	initialAssignToNearestCluster() Perform initial cluster assignment.
protected int	iterate(int iteration) Main loop function.
protected void	updateBounds(double[] move) Update the bounds for k-means.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Instance

buildResult, buildResult, copyMeans, distance, isSquared, meansFromSums, movedDistance, recomputeSeperation, run

Methods inherited from class java.lang.Object

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

Field Detail

upper

WritableDoubleDataStore upper

Upper bounds

lower

WritableDataStore<double[]> lower

Lower bounds

sums

double[][] sums

Sums of clusters.

newmeans

double[][] newmeans

Scratch space for new means.

sep

double[] sep

Cluster separation

Constructor Detail

Instance

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

Constructor.

Parameters:

relation - Relation

means - Initial means

Method Detail

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

initialAssignToNearestCluster

protected int initialAssignToNearestCluster()

Perform initial cluster assignment.

Returns:

Number of changes (i.e. relation size)

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

updateBounds

protected void updateBounds(double[] move)

Update the bounds for k-means.

Parameters:

move - Movement of centers

getLogger

protected Logging getLogger()

Description copied from class: AbstractKMeans.Instance

Get the class logger.

Specified by:

getLogger in class AbstractKMeans.Instance

Returns:

Logger