de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional

Class KNNKernelDensityMinimaClustering<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<Clustering<ClusterModel>>

de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering<V>

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<ClusterModel>>

public class KNNKernelDensityMinimaClustering<V extends NumberVector>
extends AbstractAlgorithm<Clustering<ClusterModel>>
implements ClusteringAlgorithm<Clustering<ClusterModel>>

Cluster one-dimensional data by splitting the data set on local minima after performing kernel density estimation.

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KNNKernelDensityMinimaClustering.Mode Estimation mode.
static class	KNNKernelDensityMinimaClustering.Parameterizer<V extends NumberVector> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and 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 and Description
KNNKernelDensityMinimaClustering(int dim, KernelDensityFunction kernel, KNNKernelDensityMinimaClustering.Mode mode, int k, int minwindow) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
Clustering<ClusterModel>	run(Relation<V> relation) Run the clustering algorithm on a data relation.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

makeParameterDistanceFunction, run

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm

run

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

run

public Clustering<ClusterModel> run(Relation<V> relation)

Run the clustering algorithm on a data relation.

Parameters:

relation - Relation

Returns:

Clustering result

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

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

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<Clustering<ClusterModel>>

Returns:

Type restriction

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<ClusterModel>>

Returns:

the static logger