Package elki.clustering.kmedoids
Class ReynoldsPAM<O>
- java.lang.Object
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- elki.clustering.kmedoids.PAM<O>
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- elki.clustering.kmedoids.ReynoldsPAM<O>
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- Type Parameters:
O
- vector datatype
- All Implemented Interfaces:
Algorithm
,ClusteringAlgorithm<Clustering<MedoidModel>>
,KMedoidsClustering<O>
@Reference(authors="A. P. Reynolds, G. Richards, B. de la Iglesia, V. J. Rayward-Smith", title="Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms", booktitle="J. Math. Model. Algorithms 5(4)", url="https://doi.org/10.1007/s10852-005-9022-1", bibkey="DBLP:journals/jmma/ReynoldsRIR06") public class ReynoldsPAM<O> extends PAM<O>
The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al.In our implementation, we could not observe a substantial improvement over the original PAM algorithm. This may be because of modern CPU architectures, where saving an addition may be neglibile compared to caching and pipelining.
Reference:
A. P. Reynolds, G. Richards, B. de la Iglesia, V. J. Rayward-Smith
Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms
J. Math. Model. Algorithms 5(4)- Since:
- 0.5.0
- Author:
- Erich Schubert
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Nested Class Summary
Nested Classes Modifier and Type Class Description protected static class
ReynoldsPAM.Instance
Instance for a single dataset.static class
ReynoldsPAM.Par<V>
Parameterization class.-
Nested classes/interfaces inherited from interface elki.Algorithm
Algorithm.Utils
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Constructor Summary
Constructors Constructor Description ReynoldsPAM(Distance<? super O> distance, int k, int maxiter, KMedoidsInitialization<O> initializer)
Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description protected Logging
getLogger()
Get the static class logger.Clustering<MedoidModel>
run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Run k-medoids clustering with a given distance query.
Not a very elegant API, but needed for some types of nested k-medoids.-
Methods inherited from class elki.clustering.kmedoids.PAM
getInputTypeRestriction, initialMedoids, run, wrapResult
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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.clustering.ClusteringAlgorithm
autorun
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Field Detail
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LOG
private static final Logging LOG
The logger for this class.
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KEY
private static final java.lang.String KEY
Key for statistics logging.
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Constructor Detail
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ReynoldsPAM
public ReynoldsPAM(Distance<? super O> distance, int k, int maxiter, KMedoidsInitialization<O> initializer)
Constructor.- Parameters:
distance
- distance functionk
- k parametermaxiter
- Maxiter parameterinitializer
- Function to generate the initial means
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Method Detail
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run
public Clustering<MedoidModel> run(Relation<O> relation, int k, DistanceQuery<? super O> distQ)
Description copied from interface:KMedoidsClustering
Run k-medoids clustering with a given distance query.
Not a very elegant API, but needed for some types of nested k-medoids.
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