Package elki.clustering.uncertain

Class CKMeans

java.lang.Object

elki.clustering.uncertain.CenterOfMassMetaClustering<Clustering<KMeansModel>>

elki.clustering.uncertain.CKMeans

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<KMeansModel>>

@Title("CK-means")
@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	Description
static class	CKMeans.Par	Parameterization class, based on k-means.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields inherited from class elki.clustering.uncertain.CenterOfMassMetaClustering

inner

Constructor Summary

Constructors

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

Method Summary

Methods inherited from class elki.clustering.uncertain.CenterOfMassMetaClustering

getInputTypeRestriction, run, runClusteringAlgorithm

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

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(NumberVectorDistance<? super NumberVector> distance,
               int k,
               int maxiter,
               KMeansInitialization initializer)

Constructor that uses Lloyd's k-means algorithm.

Parameters:

distance - Distance functions for centers

k - K parameter

maxiter - Maximum number of iterations

initializer - Initializer