Class FasterMSC<O>
- java.lang.Object
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- elki.clustering.kmedoids.PAM<O>
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- elki.clustering.silhouette.PAMSIL<O>
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- elki.clustering.silhouette.PAMMEDSIL<O>
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- elki.clustering.silhouette.FastMSC<O>
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- elki.clustering.silhouette.FasterMSC<O>
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- Type Parameters:
O
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- All Implemented Interfaces:
Algorithm
,ClusteringAlgorithm<Clustering<MedoidModel>>
,KMedoidsClustering<O>
@Reference(authors="Lars Lenssen and Erich Schubert", title="Clustering by Direct Optimization of the Medoid Silhouette", booktitle="Int. Conf. on Similarity Search and Applications, SISAP 2022", url="https://doi.org/10.1007/978-3-031-17849-8_15", bibkey="DBLP:conf/sisap/LenssenS22") @Priority(200) public class FasterMSC<O> extends FastMSC<O>
Fast and Eager Medoid Silhouette Clustering.This clustering algorithm tries to find an optimal silhouette clustering for an approximation to the silhouette called "medoid silhouette" using a swap-based heuristic similar to PAM. By also caching the distance to the third nearest center (compare to FastPAM, which only used the second nearest), we are able to reduce the runtime per iteration to just O(n²), which yields an acceptable run time for many use cases, while often finding a solution with better silhouette than other clustering methods. This version also performs eager swapping instead of a steepest descent, i.e., it performs any swap that improves the medoid silhouette immediately, and hence may need fewer iterations.
Reference:
Lars Lenssen and Erich Schubert
Clustering by Direct Optimization of the Medoid Silhouette
Int. Conf. on Similarity Search and Applications, SISAP 2022- Author:
- Erich Schubert
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Nested Class Summary
Nested Classes Modifier and Type Class Description protected class
FasterMSC.Instance
FasterMSC clustering instance for a particular data set.protected class
FasterMSC.Instance2
FasterMSC clustering instance for k=2, simplified.static class
FasterMSC.Par<O>
Parameterization class.-
Nested classes/interfaces inherited from class elki.clustering.silhouette.FastMSC
FastMSC.Record
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Nested classes/interfaces inherited from interface elki.Algorithm
Algorithm.Utils
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Constructor Summary
Constructors Constructor Description FasterMSC(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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Constructor Detail
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FasterMSC
public FasterMSC(Distance<? super O> distance, int k, int maxiter, KMedoidsInitialization<O> initializer)
Constructor.- Parameters:
distance
- Distance functionk
- Number of clustermaxiter
- Maximum number of iterationsinitializer
- Initialization
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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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