Package elki.clustering.onedimensional

Class KNNKernelDensityMinimaClustering

java.lang.Object

elki.clustering.onedimensional.KNNKernelDensityMinimaClustering

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

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

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.

Constructor Summary

Constructors

Constructor	Description
KNNKernelDensityMinimaClustering(int dim, KernelDensityFunction kernel, KNNKernelDensityMinimaClustering.Mode mode, int k, int minwindow)	Constructor.

Method Summary

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm

autorun

Field Detail

LOG

private static final Logging LOG

Class logger.

dim

protected int dim

Dimension to use for clustering.

kernel

protected KernelDensityFunction kernel

Kernel density function.

mode

protected KNNKernelDensityMinimaClustering.Mode mode

Estimation modes.

k

protected int k

Number of neighbors to use for bandwidth.

minwindow

protected int minwindow

Window width, for local minima criterions.

Constructor Detail

KNNKernelDensityMinimaClustering

public KNNKernelDensityMinimaClustering(int dim,
                                        KernelDensityFunction kernel,
                                        KNNKernelDensityMinimaClustering.Mode mode,
                                        int k,
                                        int minwindow)

Constructor.

Parameters:

dim - Dimension to use for clustering

kernel - Kernel function

mode - Bandwidth mode

k - Number of neighbors

minwindow - Window size for comparison

Method Detail

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from interface: Algorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Returns:

Type restriction

run

public Clustering<ClusterModel> run(Relation<? extends NumberVector> relation)

Run the clustering algorithm on a data relation.

Parameters:

relation - Relation

Returns:

Clustering result