de.lmu.ifi.dbs.elki.algorithm.clustering
Class KMeans<D extends Distance<D>,V extends NumberVector<V,?>>

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<O,R>
          de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm<V,D,Clustering<MeanModel<V>>>
              de.lmu.ifi.dbs.elki.algorithm.clustering.KMeans<D,V>

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<V,Clustering<MeanModel<V>>>, ClusteringAlgorithm<Clustering<MeanModel<V>>,V>, 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<D extends Distance<D>,V extends NumberVector<V,?>>
extends DistanceBasedAlgorithm<V,D,Clustering<MeanModel<V>>>
implements ClusteringAlgorithm<Clustering<MeanModel<V>>,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.

Author:

Arthur Zimek

Field Summary

private int	k Holds the value of K_PARAM.
static OptionID	K_ID OptionID for K_PARAM
private IntParameter	K_PARAM Parameter to specify the number of clusters to find, must be an integer greater than 0.
private int	maxiter Holds the value of MAXITER_PARAM.
static OptionID	MAXITER_ID OptionID for MAXITER_PARAM
private IntParameter	MAXITER_PARAM Parameter to specify the number of clusters to find, must be an integer greater or equal to 0, where 0 means no limit.

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

DISTANCE_FUNCTION_ID, DISTANCE_FUNCTION_PARAM

Fields inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debug, logger

Constructor Summary

KMeans(Parameterization config)
Constructor, adhering to Parameterizable

Method Summary

protected List<V>	means(List<List<Integer>> clusters, List<V> means, Database<V> database) Returns the mean vectors of the given clusters in the given database.
protected Clustering<MeanModel<V>>	runInTime(Database<V> database) Performs the k-means algorithm on the given database.
protected List<List<Integer>>	sort(List<V> means, Database<V> database) Returns a list of clusters.

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

getDistanceFactory, getDistanceFunction

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

isTime, isVerbose, run, setTime, setVerbose

Methods inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debugFine, debugFiner, debugFinest, exception, progress, verbose, warning

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.Algorithm

setTime, setVerbose

Field Detail

K_ID

public static final OptionID K_ID

OptionID for K_PARAM

K_PARAM

private final IntParameter K_PARAM

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

Key: -kmeans.k

MAXITER_ID

public static final OptionID MAXITER_ID

OptionID for MAXITER_PARAM

MAXITER_PARAM

private final IntParameter MAXITER_PARAM

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

Key: -kmeans.maxiter

k

private int k

Holds the value of K_PARAM.

maxiter

private int maxiter

Holds the value of MAXITER_PARAM.

Constructor Detail

KMeans

public KMeans(Parameterization config)

Constructor, adhering to Parameterizable

Parameters:

config - Parameterization

Method Detail

runInTime

protected Clustering<MeanModel<V>> runInTime(Database<V> database)
                                                                throws IllegalStateException

Performs the k-means algorithm on the given database.

Specified by:

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

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm

Throws:

IllegalStateException - if the algorithm has not been initialized properly (e.g. the setParameters(String[]) method has been failed to be called).

means

protected List<V> means(List<List<Integer>> clusters,
                        List<V> means,
                        Database<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<List<Integer>> sort(List<V> means,
                                   Database<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