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

Class KMeansAnnulus<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.KMeansHamerly<V>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansAnnulus<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

@Reference(authors="J. Drake",title="Faster k-means clustering",booktitle="Faster k-means clustering",url="http://hdl.handle.net/2104/8826",bibkey="mathesis/Drake13") @Reference(authors="G. Hamerly and J. Drake",title="Accelerating Lloyd\u2019s Algorithm for k-Means Clustering",booktitle="Partitional Clustering Algorithms",url="https://doi.org/10.1007/978-3-319-09259-1_2",bibkey="doi:10.1007/978-3-319-09259-1_2")
public class KMeansAnnulus<V extends NumberVector>
extends KMeansHamerly<V>

Annulus k-means algorithm. A variant of Hamerly with an additional bound, based on comparing the norm of the mean and the norm of the points.

This implementation could be further improved by precomputing and storing the norms of all points (at the cost of O(n) memory additionally).

Reference:

J. Drake
Faster k-means clustering
Masters Thesis

G. Hamerly and J. Drake
Accelerating Lloyd’s Algorithm for k-Means Clustering
Partitional Clustering Algorithms

Since:

0.7.5

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected static class	KMeansAnnulus.Instance Inner instance, storing state for a single data set.
static class	KMeansAnnulus.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.KMeansHamerly

varstat

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
KMeansAnnulus(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer, boolean varstat) 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

KMeansAnnulus

public KMeansAnnulus(NumberVectorDistanceFunction<? super V> distanceFunction,
                     int k,
                     int maxiter,
                     KMeansInitialization initializer,
                     boolean varstat)

Constructor.

Parameters:

distanceFunction - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

varstat - Compute the variance statistic

Method Detail

run

public Clustering<KMeansModel> run(Database database,
                                   Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Specified by:

run in interface KMeans<V extends NumberVector,KMeansModel>

Overrides:

run in class KMeansHamerly<V extends NumberVector>

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.

Overrides:

getLogger in class KMeansHamerly<V extends NumberVector>

Returns:

the static logger