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

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

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractPrimitiveDistanceBasedAlgorithm<V,D,Clustering<MeanModel<V>>>

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

Type Parameters:

D - a type of Distance as returned by the used distance function

V - a type of NumberVector as a suitable datatype for this algorithm

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<MeanModel<V>>>, 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 KMeans<V extends NumberVector<V,?>,D extends Distance<D>>
extends AbstractPrimitiveDistanceBasedAlgorithm<V,D,Clustering<MeanModel<V>>>
implements ClusteringAlgorithm<Clustering<MeanModel<V>>>

Provides the k-means algorithm.

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	KMeans.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private int	k Holds the value of K_ID.
static OptionID	K_ID Parameter to specify the number of clusters to find, must be an integer greater than 0.
private static Logging	logger The logger for this class.
private int	maxiter Holds the value of MAXITER_ID.
static OptionID	MAXITER_ID Parameter to specify the number of clusters to find, must be an integer greater or equal to 0, where 0 means no limit.
private Long	seed Holds the value of SEED_ID.
static OptionID	SEED_ID Parameter to specify the random generator seed.

Constructor Summary

Constructors

Constructor and Description
KMeans(PrimitiveDistanceFunction<? super V,D> distanceFunction, int k, int maxiter, Long seed) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
protected List<V>	means(List<? extends ModifiableDBIDs> clusters, List<V> means, Relation<V> database) Returns the mean vectors of the given clusters in the given database.
Clustering<MeanModel<V>>	run(Database database, Relation<V> relation) Run k-means
protected List<? extends ModifiableDBIDs>	sort(List<V> means, Relation<V> database) Returns a list of clusters.

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

logger

private static final Logging logger

The logger for this class.

K_ID

public static final OptionID K_ID

Parameter to specify the number of clusters to find, must be an integer greater than 0.

MAXITER_ID

public static final OptionID MAXITER_ID

Parameter to specify the number of clusters to find, must be an integer greater or equal to 0, where 0 means no limit.

SEED_ID

public static final OptionID SEED_ID

Parameter to specify the random generator seed.

k

private int k

Holds the value of K_ID.

maxiter

private int maxiter

Holds the value of MAXITER_ID.

seed

private Long seed

Holds the value of SEED_ID.

Constructor Detail

KMeans

public KMeans(PrimitiveDistanceFunction<? super V,D> distanceFunction,
      int k,
      int maxiter,
      Long seed)

Constructor.

Parameters:

distanceFunction - distance function

k - k parameter

maxiter - Maxiter parameter

seed - Random generator seed

Method Detail

run

public Clustering<MeanModel<V>> run(Database database,
                           Relation<V> relation)
                                                       throws IllegalStateException

Run k-means

Parameters:

database - Database

relation - relation to use

Returns:

result

Throws:

IllegalStateException

means

protected List<V> means(List<? extends ModifiableDBIDs> clusters,
            List<V> means,
            Relation<V> database)

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

Parameters:

clusters - the clusters to compute the means

means - the recent means

database - the database containing the vectors

Returns:

the mean vectors of the given clusters in the given database

sort

protected List<? extends ModifiableDBIDs> sort(List<V> means,
                                   Relation<V> database)

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

Parameters:

means - a list of k means

database - the database to cluster

Returns:

list of k clusters

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<Clustering<MeanModel<V extends NumberVector<V,?>>>>

Returns:

Type restriction

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<MeanModel<V extends NumberVector<V,?>>>>

Returns:

the static logger