de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch

Class CentroidManhattanDistance

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.CentroidManhattanDistance

All Implemented Interfaces:

BIRCHDistance

@Alias(value="D1")
 @Reference(authors="T. Zhang",
           title="Data Clustering for Very Large Datasets Plus Applications",
           booktitle="University of Wisconsin Madison, Technical Report #1355",
           url="ftp://ftp.cs.wisc.edu/pub/techreports/1997/TR1355.pdf",
           bibkey="tr/wisc/Zhang97")
public class CentroidManhattanDistance
extends java.lang.Object
implements BIRCHDistance

Centroid Manhattan Distance

Reference:

Data Clustering for Very Large Datasets Plus Applications
T. Zhang
Doctoral Dissertation, 1997.

Since:

0.7.5

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	CentroidManhattanDistance.Parameterizer Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
static CentroidManhattanDistance	STATIC Static instance.

Constructor Summary

Constructors

Constructor and Description
CentroidManhattanDistance()

Method Summary

Modifier and Type	Method and Description
double	squaredDistance(ClusteringFeature v, ClusteringFeature cf) Distance between two clustering features.
double	squaredDistance(NumberVector v, ClusteringFeature cf) Distance of a vector to a clustering feature.

Methods inherited from class java.lang.Object

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

Field Detail

STATIC

public static final CentroidManhattanDistance STATIC

Static instance.

Constructor Detail

CentroidManhattanDistance

public CentroidManhattanDistance()

Method Detail

squaredDistance

public double squaredDistance(NumberVector v,
                              ClusteringFeature cf)

Description copied from interface: BIRCHDistance

Distance of a vector to a clustering feature.

Specified by:

squaredDistance in interface BIRCHDistance

Parameters:

v - Vector

cf - Clustering Feature

Returns:

Distance

squaredDistance

public double squaredDistance(ClusteringFeature v,
                              ClusteringFeature cf)

Description copied from interface: BIRCHDistance

Distance between two clustering features.

Specified by:

squaredDistance in interface BIRCHDistance

Parameters:

v - First clustering feature

cf - Second clustering feature

Returns:

Distance