Package elki.clustering.kmeans

Class MacQueenKMeans<V extends NumberVector>

java.lang.Object

elki.clustering.kmeans.AbstractKMeans<V,KMeansModel>

elki.clustering.kmeans.MacQueenKMeans<V>

Type Parameters:

V - vector type to use

All Implemented Interfaces:

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

@Title("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 MacQueenKMeans<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	Description
protected static class	MacQueenKMeans.Instance	Inner instance, storing state for a single data set.
static class	MacQueenKMeans.Par<V extends NumberVector>	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private static Logging	LOG	The logger for this class.

Fields inherited from class elki.clustering.kmeans.AbstractKMeans

distance, initializer, k, maxiter

Fields inherited from interface elki.clustering.kmeans.KMeans

DISTANCE_FUNCTION_ID, INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

Constructor Summary

Constructors

Constructor	Description
MacQueenKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected Logging	getLogger()	Get the (STATIC) logger for this class.
Clustering<KMeansModel>	run(Relation<V> relation)	Run the clustering algorithm.

Methods inherited from class elki.clustering.kmeans.AbstractKMeans

getDistance, getInputTypeRestriction, incrementalUpdateMean, initialMeans, means, minusEquals, nearestMeans, plusEquals, plusMinusEquals, setDistance, setInitializer, setK

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm

autorun

Field Detail

LOG

private static final Logging LOG

The logger for this class.

Constructor Detail

MacQueenKMeans

public MacQueenKMeans(NumberVectorDistance<? super V> distance,
                      int k,
                      int maxiter,
                      KMeansInitialization initializer)

Constructor.

Parameters:

distance - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

Method Detail

run

public Clustering<KMeansModel> run(Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Parameters:

relation - Relation to process.

Returns:

Clustering result

getLogger

protected Logging getLogger()

Description copied from class: AbstractKMeans

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractKMeans<V extends NumberVector,KMeansModel>

Returns:

the static logger