Class CFRandomlyChosen
- java.lang.Object
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- elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization
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- elki.clustering.kmeans.initialization.betula.CFRandomlyChosen
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@Reference(authors="Andreas Lang and Erich Schubert", title="BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees", booktitle="Information Systems", url="https://doi.org/10.1016/j.is.2021.101918", bibkey="DBLP:journals/is/LangS22") public class CFRandomlyChosen extends AbstractCFKMeansInitialization
Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features. For normal k-means useRandomlyChosen
.References:
Andreas Lang and Erich Schubert
BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees
Information Systems- Since:
- 0.8.0
- Author:
- Andreas Lang
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
CFRandomlyChosen.Par
Parameterization class.
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Field Summary
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Fields inherited from class elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization
rf
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Constructor Summary
Constructors Constructor Description CFRandomlyChosen(RandomFactory rf)
Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description double[][]
chooseInitialMeans(CFTree<?> tree, java.util.List<? extends ClusterFeature> cfs, int k)
Build the initial models.
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Constructor Detail
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CFRandomlyChosen
public CFRandomlyChosen(RandomFactory rf)
Constructor.- Parameters:
rf
- Random generator
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Method Detail
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chooseInitialMeans
public double[][] chooseInitialMeans(CFTree<?> tree, java.util.List<? extends ClusterFeature> cfs, int k)
Description copied from class:AbstractCFKMeansInitialization
Build the initial models.- Specified by:
chooseInitialMeans
in classAbstractCFKMeansInitialization
- Parameters:
tree
- CF treecfs
- Cluster features of the tree (may be ignored for tree-based initializations, should be an array list for efficiency)k
- Number of clusters.- Returns:
- initial cluster means
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