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

Class KMeansMacQueen<V extends NumberVector<?>,D extends Distance<D>>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractPrimitiveDistanceBasedAlgorithm<NumberVector<?>,D,Clustering<M>>

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

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen<V,D>

Type Parameters:

V - vector type to use

D - distance function value type

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<KMeansModel<V>>>, KMeans, InspectionUtilFrequentlyScanned, Parameterizable

@Title(value="K-Means")
@Description(value="Finds a partitioning into k clusters.")
@Reference(authors="J. MacQueen",
           title="Some Methods for Classification and Analysis of Multivariate Observations",
           booktitle="5th Berkeley Symp. Math. Statist. Prob., Vol. 1, 1967, pp 281-297",
           url="http://projecteuclid.org/euclid.bsmsp/1200512992")
public class KMeansMacQueen<V extends NumberVector<?>,D extends Distance<D>>
extends AbstractKMeans<V,D,KMeansModel<V>>

Provides the k-means algorithm, using MacQueen style incremental updates.

Reference:
J. MacQueen: Some Methods for Classification and Analysis of Multivariate Observations.
In 5th Berkeley Symp. Math. Statist. Prob., Vol. 1, 1967, pp 281-297.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMeansMacQueen.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
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 interface de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans

INIT_ID, K_ID, MAXITER_ID, SEED_ID

Constructor Summary

Constructors

Constructor and Description
KMeansMacQueen(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
protected Logging	getLogger() Get the (STATIC) logger for this class.
Clustering<KMeansModel<V>>	run(Database database, Relation<V> relation) Run k-means.

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

assignToNearestCluster, getInputTypeRestriction, incrementalUpdateMean, macQueenIterate, means, medians

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

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

Field Detail

LOG

private static final Logging LOG

The logger for this class.

Constructor Detail

KMeansMacQueen

public KMeansMacQueen(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction,
              int k,
              int maxiter,
              KMeansInitialization<V> initializer)

Constructor.

Parameters:

distanceFunction - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

Method Detail

run

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

Run k-means.

Parameters:

database - Database

relation - relation to use

Returns:

Clustering result

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<KMeansModel<V extends NumberVector<?>>>>

Returns:

the static logger