Package elki.clustering.kmeans

Class CompareMeans.Instance

java.lang.Object

elki.clustering.kmeans.AbstractKMeans.Instance

elki.clustering.kmeans.CompareMeans.Instance

Direct Known Subclasses:

SortMeans.Instance

Enclosing class:

CompareMeans<V extends NumberVector>

protected static class CompareMeans.Instance
extends AbstractKMeans.Instance

Inner instance, storing state for a single data set.

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field	Description
(package private) double[][]	cdist	Cluster center distances.

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)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected int	assignToNearestCluster()	Assign each object to the nearest cluster.
protected Logging	getLogger()	Get the class logger.
protected int	iterate(int iteration)	Main loop function.
protected void	recomputeSeperation(double[][] means, double[][] cdist)	Recompute the separation of cluster means.

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

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

Methods inherited from class java.lang.Object

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

Field Detail

cdist

double[][] cdist

Cluster center distances.

Constructor Detail

Instance

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

Constructor.

Parameters:

relation - Relation

df - Distance function

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

recomputeSeperation

protected void recomputeSeperation(double[][] means,
                                   double[][] cdist)

Recompute the separation of cluster means.

Used by Sort and Compare variants.

Parameters:

means - Means

cdist - Center-to-Center distances (half-sqrt scaled)

assignToNearestCluster

protected int assignToNearestCluster()

Description copied from class: AbstractKMeans.Instance

Assign each object to the nearest cluster.

Overrides:

assignToNearestCluster in class AbstractKMeans.Instance

Returns:

number of objects reassigned

getLogger

protected Logging getLogger()

Description copied from class: AbstractKMeans.Instance

Get the class logger.

Specified by:

getLogger in class AbstractKMeans.Instance

Returns:

Logger