Package elki.clustering.kmeans.initialization.betula

Class InterclusterWeight

java.lang.Object

elki.clustering.kmeans.initialization.betula.InterclusterWeight

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 InterclusterWeight
extends java.lang.Object
implements CFInitWeight

Initialization via n2 * D2²(cf1, cf2), which supposedly is closes to the idea of k-means++ initialization.

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
InterclusterWeight()

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

InterclusterWeight

public InterclusterWeight()

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