Class CentroidManhattanDistance
- java.lang.Object
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- elki.clustering.hierarchical.birch.CentroidManhattanDistance
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- All Implemented Interfaces:
BIRCHDistance
@Alias("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 DistanceReference:
Data Clustering for Very Large Datasets Plus Applications
T. Zhang
Doctoral Dissertation, 1997.- Since:
- 0.7.5
- Author:
- Erich Schubert
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
CentroidManhattanDistance.Par
Parameterization class.
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Field Summary
Fields Modifier and Type Field Description static CentroidManhattanDistance
STATIC
Static instance.
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Constructor Summary
Constructors Constructor Description CentroidManhattanDistance()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method 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.
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Field Detail
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STATIC
public static final CentroidManhattanDistance STATIC
Static instance.
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Method Detail
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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 interfaceBIRCHDistance
- Parameters:
v
- Vectorcf
- Clustering Feature- Returns:
- Distance
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squaredDistance
public double squaredDistance(ClusteringFeature v, ClusteringFeature cf)
Description copied from interface:BIRCHDistance
Distance between two clustering features.- Specified by:
squaredDistance
in interfaceBIRCHDistance
- Parameters:
v
- First clustering featurecf
- Second clustering feature- Returns:
- Distance
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