Package elki.clustering.onedimensional
Class KNNKernelDensityMinimaClustering
- java.lang.Object
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- elki.clustering.onedimensional.KNNKernelDensityMinimaClustering
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- All Implemented Interfaces:
Algorithm
,ClusteringAlgorithm<Clustering<ClusterModel>>
public class KNNKernelDensityMinimaClustering extends java.lang.Object implements ClusteringAlgorithm<Clustering<ClusterModel>>
Cluster one-dimensional data by splitting the data set on local minima after performing kernel density estimation.- Since:
- 0.6.0
- Author:
- Erich Schubert
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
KNNKernelDensityMinimaClustering.Mode
Estimation mode.static class
KNNKernelDensityMinimaClustering.Par
Parameterization class.-
Nested classes/interfaces inherited from interface elki.Algorithm
Algorithm.Utils
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Field Summary
Fields Modifier and Type Field Description protected int
dim
Dimension to use for clustering.protected int
k
Number of neighbors to use for bandwidth.protected KernelDensityFunction
kernel
Kernel density function.private static Logging
LOG
Class logger.protected int
minwindow
Window width, for local minima criterions.protected KNNKernelDensityMinimaClustering.Mode
mode
Estimation modes.
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Constructor Summary
Constructors Constructor Description KNNKernelDensityMinimaClustering(int dim, KernelDensityFunction kernel, KNNKernelDensityMinimaClustering.Mode mode, int k, int minwindow)
Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description TypeInformation[]
getInputTypeRestriction()
Get the input type restriction used for negotiating the data query.Clustering<ClusterModel>
run(Relation<? extends NumberVector> relation)
Run the clustering algorithm on a data relation.-
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
Class logger.
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dim
protected int dim
Dimension to use for clustering.
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kernel
protected KernelDensityFunction kernel
Kernel density function.
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mode
protected KNNKernelDensityMinimaClustering.Mode mode
Estimation modes.
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k
protected int k
Number of neighbors to use for bandwidth.
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minwindow
protected int minwindow
Window width, for local minima criterions.
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Constructor Detail
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KNNKernelDensityMinimaClustering
public KNNKernelDensityMinimaClustering(int dim, KernelDensityFunction kernel, KNNKernelDensityMinimaClustering.Mode mode, int k, int minwindow)
Constructor.- Parameters:
dim
- Dimension to use for clusteringkernel
- Kernel functionmode
- Bandwidth modek
- Number of neighborsminwindow
- Window size for comparison
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Method Detail
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getInputTypeRestriction
public TypeInformation[] getInputTypeRestriction()
Description copied from interface:Algorithm
Get the input type restriction used for negotiating the data query.- Specified by:
getInputTypeRestriction
in interfaceAlgorithm
- Returns:
- Type restriction
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run
public Clustering<ClusterModel> run(Relation<? extends NumberVector> relation)
Run the clustering algorithm on a data relation.- Parameters:
relation
- Relation- Returns:
- Clustering result
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