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

Class CKMeans

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<C>

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.CenterOfMassMetaClustering<Clustering<KMeansModel>>

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.CKMeans

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<KMeansModel>>

@Reference(authors="S. D. Lee, B. Kao, R. Cheng",
           title="Reducing UK-means to K-means",
           booktitle="ICDM Data Mining Workshops, 2007",
           url="https://doi.org/10.1109/ICDMW.2007.40",
           bibkey="DBLP:conf/icdm/LeeKC07")
public class CKMeans
extends CenterOfMassMetaClustering<Clustering<KMeansModel>>

Run k-means on the centers of each uncertain object.

This is a baseline reference method, that computes the center of mass (centroid) of each object, then runs k-means on this.

References:

This algorithm was introduced as CK-Means in:

S. D. Lee, B. Kao, R. Cheng
Reducing UK-means to K-means
ICDM Data Mining Workshops, 2007

and was shown to be equivalent to UK-Means.

In summary, the expected distance used by UK-Means can be decomposed using Steiner/König-Huygens into the sum of squares between the centroids, and the sum of squared deviations within the uncertain object itself. This last term, however, is constant.

Since:

0.7.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	CKMeans.Parameterizer Parameterization class, based on k-means.

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG CLass logger.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.CenterOfMassMetaClustering

inner

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

ALGORITHM_ID

Constructor Summary

Constructors

Constructor and Description
CKMeans(KMeans<?,KMeansModel> kmeans) Constructor that uses an arbitrary k-means algorithm.
CKMeans(NumberVectorDistanceFunction<? super NumberVector> distanceFunction, int k, int maxiter, KMeansInitialization initializer) Constructor that uses Lloyd's k-means algorithm.

Method Summary

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

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.CenterOfMassMetaClustering

getInputTypeRestriction, run, runClusteringAlgorithm

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

Field Detail

LOG

private static final Logging LOG

CLass logger.

Constructor Detail

CKMeans

public CKMeans(KMeans<?,KMeansModel> kmeans)

Constructor that uses an arbitrary k-means algorithm.

Parameters:

kmeans - K-Means algorithm to use.

CKMeans

public CKMeans(NumberVectorDistanceFunction<? super NumberVector> distanceFunction,
               int k,
               int maxiter,
               KMeansInitialization initializer)

Constructor that uses Lloyd's k-means algorithm.

Parameters:

distanceFunction - Distance functions for centers

k - K parameter

maxiter - Maximum number of iterations

initializer - Initializer

Method Detail

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Overrides:

getLogger in class CenterOfMassMetaClustering<Clustering<KMeansModel>>

Returns:

the static logger