Package elki.clustering.kmeans

Class KMeansMinusMinus.Instance

java.lang.Object

elki.clustering.kmeans.AbstractKMeans.Instance

elki.clustering.kmeans.KMeansMinusMinus.Instance

Enclosing class:

KMeansMinusMinus<V extends elki.data.NumberVector>

protected class KMeansMinusMinus.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) java.util.List<elki.database.ids.ModifiableDoubleDBIDList>	clusters	Cluster storage.
(package private) int	heapsize	Desired size of the heap.
(package private) elki.utilities.datastructures.heap.DoubleMinHeap	minHeap	Heap of the noise candidates.
(package private) double	prevvartotal	Variance of the previous iteration

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

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

Constructor Summary

Constructors

Constructor	Description
Instance(elki.database.relation.Relation<? extends elki.data.NumberVector> relation, elki.distance.NumberVectorDistance<?> df, double[][] means)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected int	assignToNearestCluster()	Returns a list of clusters.
protected Clustering<KMeansModel>	buildResultWithNoise()
protected elki.logging.Logging	getLogger()	Get the class logger.
protected int	iterate(int iteration)	Main loop function.
protected double[][]	meansWithTreshhold(double tresh)	Returns the mean vectors of the given clusters in the given database.

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

minHeap

elki.utilities.datastructures.heap.DoubleMinHeap minHeap

Heap of the noise candidates.

heapsize

int heapsize

Desired size of the heap.

prevvartotal

double prevvartotal

Variance of the previous iteration

clusters

java.util.List<elki.database.ids.ModifiableDoubleDBIDList> clusters

Cluster storage.

Constructor Detail

Instance

public Instance(elki.database.relation.Relation<? extends elki.data.NumberVector> relation,
                elki.distance.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

buildResultWithNoise

protected Clustering<KMeansModel> buildResultWithNoise()

assignToNearestCluster

protected int assignToNearestCluster()

Returns a list of clusters. The k^th cluster contains the ids of those FeatureVectors, that are nearest to the k^th mean. And saves the distance in a MinHeap.

Overrides:

assignToNearestCluster in class AbstractKMeans.Instance

Returns:

the number of reassigned objects

meansWithTreshhold

protected double[][] meansWithTreshhold(double tresh)

Returns the mean vectors of the given clusters in the given database.

Parameters:

tresh - Threshold

Returns:

the mean vectors of the given clusters in the given database

getLogger

protected elki.logging.Logging getLogger()

Description copied from class: AbstractKMeans.Instance

Get the class logger.

Specified by:

getLogger in class AbstractKMeans.Instance

Returns:

Logger