Package elki.index.tree.betula.distance

Class BIRCHRadiusDistance

java.lang.Object

elki.index.tree.betula.distance.BIRCHRadiusDistance

All Implemented Interfaces:

CFDistance

@Priority(-100)
@Reference(authors="T. Zhang, R. Ramakrishnan, M. Livny",
           title="BIRCH: An Efficient Data Clustering Method for Very Large Databases",
           booktitle="Proc. 1996 ACM SIGMOD International Conference on Management of Data",
           url="https://doi.org/10.1145/233269.233324",
           bibkey="DBLP:conf/sigmod/ZhangRL96")
public class BIRCHRadiusDistance
extends java.lang.Object
implements CFDistance

Average Radius (R) criterion.

References:

T. Zhang, R. Ramakrishnan, M. Livny
BIRCH: An Efficient Data Clustering Method for Very Large Databases
Proc. 1996 ACM SIGMOD International Conference on Management of Data

Since:

0.8.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	BIRCHRadiusDistance.Par	Parameterization class

Field Summary

Fields

Modifier and Type	Field	Description
static BIRCHRadiusDistance	STATIC	Static instance.

Constructor Summary

Constructors

Constructor	Description
BIRCHRadiusDistance()

Method Summary

Modifier and Type	Method	Description
double	squaredDistance(NumberVector n, ClusterFeature ocf)	Distance of a vector to a clustering feature.
double	squaredDistance(ClusterFeature ocf1, ClusterFeature ocf2)	Distance between two clustering features.

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.index.tree.betula.distance.CFDistance

matSelfInit

Field Detail

STATIC

public static final BIRCHRadiusDistance STATIC

Static instance.

Constructor Detail

BIRCHRadiusDistance

public BIRCHRadiusDistance()

Method Detail

squaredDistance

public double squaredDistance(NumberVector n,
                              ClusterFeature ocf)

Description copied from interface: CFDistance

Distance of a vector to a clustering feature.

Specified by:

squaredDistance in interface CFDistance

Parameters:

n - Vector

ocf - Clustering Feature

Returns:

Distance

squaredDistance

public double squaredDistance(ClusterFeature ocf1,
                              ClusterFeature ocf2)

Description copied from interface: CFDistance

Distance between two clustering features.

Specified by:

squaredDistance in interface CFDistance

Parameters:

ocf1 - First clustering feature

ocf2 - Second clustering feature

Returns:

Distance