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

Class KMeansCompare<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm<V,Clustering<M>>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans<V,KMeansModel>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansCompare<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<KMeansModel>>, KMeans<V,KMeansModel>, DistanceBasedAlgorithm<V>

@Title(value="Compare-Means")
@Reference(authors="S. J. Phillips",
           title="Acceleration of k-means and related clustering algorithms",
           booktitle="Proc. 4th Int. Workshop on Algorithm Engineering and Experiments (ALENEX 2002)",
           url="http://dx.doi.org/10.1007/3-540-45643-0_13")
public class KMeansCompare<V extends NumberVector>
extends AbstractKMeans<V,KMeansModel>

Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means. Reference:

S. J. Phillips
Acceleration of k-means and related clustering algorithms
Proc. 4th Int. Workshop on Algorithm Engineering and Experiments (ALENEX 2002)

Since:

0.5.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMeansCompare.Parameterizer<V extends NumberVector> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private static String	KEY Key for statistics logging.
private static Logging	LOG The logger for this class.

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

initializer, k, maxiter

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm

distanceFunction

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans

INIT_ID, K_ID, MAXITER_ID, SEED_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
KMeansCompare(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private boolean	assignToNearestCluster(Relation<V> relation, List<Vector> means, List<ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] varsum, double[][] cdist, LongStatistic diststat) Reassign objects, but only if their bounds indicate it is necessary to do so.
protected Logging	getLogger() Get the (STATIC) logger for this class.
private void	recomputeSeperation(List<Vector> means, double[][] cdist, LongStatistic diststat) Recompute the separation of cluster means.
Clustering<KMeansModel>	run(Database database, Relation<V> relation) Run the clustering algorithm.

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

assignToNearestCluster, getInputTypeRestriction, incrementalUpdateMean, logVarstat, macQueenIterate, means, medians, setDistanceFunction, setK

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm

getDistanceFunction

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

makeParameterDistanceFunction, run

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm

run

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

getDistanceFunction

Field Detail

LOG

private static final Logging LOG

The logger for this class.

KEY

private static final String KEY

Key for statistics logging.

Constructor Detail

KMeansCompare

public KMeansCompare(NumberVectorDistanceFunction<? super V> distanceFunction,
             int k,
             int maxiter,
             KMeansInitialization<? super V> initializer)

Constructor.

Parameters:

distanceFunction - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

Method Detail

run

public Clustering<KMeansModel> run(Database database,
                          Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Parameters:

database - Database to run on.

relation - Relation to process.

Returns:

Clustering result

recomputeSeperation

private void recomputeSeperation(List<Vector> means,
                       double[][] cdist,
                       LongStatistic diststat)

Recompute the separation of cluster means.

Parameters:

means - Means

cdist - Center-to-Center distances

diststat - Distance counting statistic

assignToNearestCluster

private boolean assignToNearestCluster(Relation<V> relation,
                             List<Vector> means,
                             List<ModifiableDBIDs> clusters,
                             WritableIntegerDataStore assignment,
                             double[] varsum,
                             double[][] cdist,
                             LongStatistic diststat)

Reassign objects, but only if their bounds indicate it is necessary to do so.

Parameters:

relation - Data

means - Current means

clusters - Current clusters

assignment - Cluster assignment

varsum - Variance sum counter

cdist - Centroid distances

diststat - Distance statistics

Returns:

true when the object was reassigned

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<KMeansModel>>

Returns:

the static logger