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

Class KMeansMacQueen<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.KMeansMacQueen<V>

Type Parameters:

V - vector type to use

All Implemented Interfaces:

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

@Title(value="k-Means (MacQueen Algorithm)")
 @Reference(authors="J. MacQueen",
           title="Some Methods for Classification and Analysis of Multivariate Observations",
           booktitle="5th Berkeley Symp. Math. Statist. Prob.",
           url="http://projecteuclid.org/euclid.bsmsp/1200512992",
           bibkey="conf/bsmsp/MacQueen67")
public class KMeansMacQueen<V extends NumberVector>
extends AbstractKMeans<V,KMeansModel>

The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.

This implementation will by default iterate over the data set until convergence, although MacQueen likely only meant to do a single pass over the data, but the result quality improves with multiple passes.

Reference:

J. MacQueen
Some Methods for Classification and Analysis of Multivariate Observations
5th Berkeley Symp. Math. Statist. Prob.

Since:

0.1

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected static class	KMeansMacQueen.Instance Inner instance, storing state for a single data set.
static class	KMeansMacQueen.Parameterizer<V extends NumberVector> 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 class de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm

distanceFunction

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

ALGORITHM_ID

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

INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
protected Logging	getLogger() Get the (STATIC) logger for this class.
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

getInputTypeRestriction, incrementalUpdateMean, initialMeans, means, minusEquals, nearestMeans, plusEquals, plusMinusEquals, setDistanceFunction, setInitializer, setK

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

getDistanceFunction

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

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.

Constructor Detail

KMeansMacQueen

public KMeansMacQueen(NumberVectorDistanceFunction<? super V> distanceFunction,
                      int k,
                      int maxiter,
                      KMeansInitialization 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

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