Class CentroidManhattanDistance
- java.lang.Object
-
- elki.clustering.hierarchical.birch.CentroidManhattanDistance
-
- 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
-
-
Nested Class Summary
Nested Classes Modifier and Type Class Description static classCentroidManhattanDistance.ParParameterization class.
-
Field Summary
Fields Modifier and Type Field Description static CentroidManhattanDistanceSTATICStatic instance.
-
Constructor Summary
Constructors Constructor Description CentroidManhattanDistance()
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description doublesquaredDistance(ClusteringFeature v, ClusteringFeature cf)Distance between two clustering features.doublesquaredDistance(NumberVector v, ClusteringFeature cf)Distance of a vector to a clustering feature.
-
-
-
Field Detail
-
STATIC
public static final CentroidManhattanDistance STATIC
Static instance.
-
-
Method Detail
-
squaredDistance
public double squaredDistance(NumberVector v, ClusteringFeature cf)
Description copied from interface:BIRCHDistanceDistance of a vector to a clustering feature.- Specified by:
squaredDistancein interfaceBIRCHDistance- Parameters:
v- Vectorcf- Clustering Feature- Returns:
- Distance
-
squaredDistance
public double squaredDistance(ClusteringFeature v, ClusteringFeature cf)
Description copied from interface:BIRCHDistanceDistance between two clustering features.- Specified by:
squaredDistancein interfaceBIRCHDistance- Parameters:
v- First clustering featurecf- Second clustering feature- Returns:
- Distance
-
-