Package elki.clustering.kmeans

Class YinYangKMeans<V extends NumberVector>

java.lang.Object

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

elki.clustering.kmeans.YinYangKMeans<V>

Type Parameters:

V - Vector type

All Implemented Interfaces:

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

@Reference(authors="Y. Ding, Y. Zhao, X. Shen, M, Musuvathi, T. Mytkowicz",
           title="Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup",
           booktitle="Proc. International Conference on Machine Learning (ICML 2015)",
           url="http://proceedings.mlr.press/v37/ding15.html",
           bibkey="DBLP:conf/icml/DingZSMM15")
public class YinYangKMeans<V extends NumberVector>
extends AbstractKMeans<V,KMeansModel>

Yin-Yang k-Means Clustering. This approach has one bound for each group of cluster centers, and sits in-between of Hamerly (one bound only) and Elkan (one bound for each center).

Reference:

Y. Ding, Y. Zhao, X. Shen, M, Musuvathi, T. Mytkowicz
Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup
Proc. International Conference on Machine Learning (ICML 2015)

Since:

0.8.0

Author:

Minh Nhat Nguyen, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
protected static class	YinYangKMeans.Instance	Instance for a particular data set.
static class	YinYangKMeans.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 int	GROUP_KMEANS_MAXITER	Use only up to 5 iterations of kmeans for grouping initial centers.
private static Logging	LOG	Class logger
private int	t	Number of cluster center groups t

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
YinYangKMeans(int k, int maxiter, KMeansInitialization initializer, int t)	Constructor.

Method Summary

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

Class logger

GROUP_KMEANS_MAXITER

private static final int GROUP_KMEANS_MAXITER

Use only up to 5 iterations of kmeans for grouping initial centers.

See Also:

Constant Field Values

t

private int t

Number of cluster center groups t

Constructor Detail

YinYangKMeans

public YinYangKMeans(int k,
                     int maxiter,
                     KMeansInitialization initializer,
                     int t)

Constructor.

Parameters:

k - Number of clusters

maxiter - Maximum number of iterations

initializer - Initialization method

t - Number of cluster center groups for pruning

Method Detail

run

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

Description copied from interface: KMeans

Run the clustering algorithm.

Parameters:

rel - 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