Package elki.index.tree.betula.distance
Class BIRCHRadiusDistance
- java.lang.Object
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- elki.index.tree.betula.distance.BIRCHRadiusDistance
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- 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
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
BIRCHRadiusDistance.Par
Parameterization class
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Field Summary
Fields Modifier and Type Field Description static BIRCHRadiusDistance
STATIC
Static instance.
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Constructor Summary
Constructors Constructor Description BIRCHRadiusDistance()
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Method Summary
All Methods Instance Methods Concrete Methods 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
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Methods inherited from interface elki.index.tree.betula.distance.CFDistance
matSelfInit
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Field Detail
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STATIC
public static final BIRCHRadiusDistance STATIC
Static instance.
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Method Detail
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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 interfaceCFDistance
- Parameters:
n
- Vectorocf
- Clustering Feature- Returns:
- Distance
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squaredDistance
public double squaredDistance(ClusterFeature ocf1, ClusterFeature ocf2)
Description copied from interface:CFDistance
Distance between two clustering features.- Specified by:
squaredDistance
in interfaceCFDistance
- Parameters:
ocf1
- First clustering featureocf2
- Second clustering feature- Returns:
- Distance
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