Package elki.clustering.kmeans.initialization.betula

Class VarianceWeight

java.lang.Object

elki.clustering.kmeans.initialization.betula.VarianceWeight

All Implemented Interfaces:

CFInitWeight

@Reference(authors="Andreas Lang and Erich Schubert",
           title="BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees",
           booktitle="Information Systems",
           url="https://doi.org/10.1016/j.is.2021.101918",
           bibkey="DBLP:journals/is/LangS22")
public class VarianceWeight
extends java.lang.Object
implements CFInitWeight

Variance-based weighting scheme for k-means clustering with BETULA.

References:

Andreas Lang and Erich Schubert
BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees
Information Systems

Since:

0.8.0

Author:

Andreas Lang

Constructor Summary

Constructors

Constructor	Description
VarianceWeight()

Method Summary

Modifier and Type	Method	Description
double	squaredWeight(ClusterFeature existing, ClusterFeature candidate)	Distance between two clustering features.

Methods inherited from class java.lang.Object

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

Constructor Detail

VarianceWeight

public VarianceWeight()

Method Detail

squaredWeight

public double squaredWeight(ClusterFeature existing,
                            ClusterFeature candidate)

Description copied from interface: CFInitWeight

Distance between two clustering features.

Specified by:

squaredWeight in interface CFInitWeight

Parameters:

existing - Previously chosen clustering feature

candidate - Candidate clustering feature

Returns:

Weight