Package elki.clustering.kmedoids
Class SingleAssignmentKMedoids<O>
- java.lang.Object
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- elki.clustering.kmedoids.PAM<O>
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- elki.clustering.kmedoids.SingleAssignmentKMedoids<O>
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- Type Parameters:
O
- object datatype
- All Implemented Interfaces:
Algorithm
,ClusteringAlgorithm<Clustering<MedoidModel>>
,KMedoidsClustering<O>
@Priority(-100) public class SingleAssignmentKMedoids<O> extends PAM<O>
K-medoids clustering by using the initialization only, then assigning each object to the nearest neighbor.- Since:
- 0.8.0
- Author:
- Erich Schubert
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Nested Class Summary
Nested Classes Modifier and Type Class Description protected static class
SingleAssignmentKMedoids.Instance
Instance for a single dataset.static class
SingleAssignmentKMedoids.Par<O>
Parameterization class.-
Nested classes/interfaces inherited from interface elki.Algorithm
Algorithm.Utils
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Constructor Summary
Constructors Constructor Description SingleAssignmentKMedoids(Distance<? super O> distance, int k, 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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Constructor Detail
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SingleAssignmentKMedoids
public SingleAssignmentKMedoids(Distance<? super O> distance, int k, KMedoidsInitialization<O> initializer)
Constructor.- Parameters:
distance
- distance functionk
- k 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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