de.lmu.ifi.dbs.elki.algorithm.clustering.subspace
Class ProjectedClustering<V extends RealVector<V,?>>

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable
          de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<V,Clustering<Model>>
              de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.ProjectedClustering<V>

Type Parameters:

V - the type of Realvector handled by this Algorithm

All Implemented Interfaces:

Algorithm<V,Clustering<Model>>, ClusteringAlgorithm<Clustering<Model>,V>, Parameterizable

Direct Known Subclasses:

ORCLUS, PROCLUS

public abstract class ProjectedClustering<V extends RealVector<V,?>>
extends AbstractAlgorithm<V,Clustering<Model>>
implements ClusteringAlgorithm<Clustering<Model>,V>

Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.

Author:

Elke Achtert

Field Summary

private EuclideanDistanceFunction<V>	distanceFunction The euclidean distance function.
private int	k Holds the value of K_PARAM.
private int	k_i Holds the value of K_I_PARAM.
static OptionID	K_I_ID OptionID for K_I_PARAM
private IntParameter	K_I_PARAM Parameter to specify the multiplier for the initial number of seeds, must be an integer greater than 0.
static OptionID	K_ID OptionID for K_PARAM
private IntParameter	K_PARAM Parameter to specify the number of clusters to find, must be an integer greater than 0.
private int	l Holds the value of L_PARAM.
static OptionID	L_ID OptionID for L_PARAM
private IntParameter	L_PARAM Parameter to specify the dimensionality of the clusters to find, must be an integer greater than 0.
private Clustering<Model>	result The result.

Fields inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable

optionHandler

Fields inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debug, logger

Constructor Summary

ProjectedClustering()
Adds parameters K_PARAM, K_I_PARAM, and L_PARAM to the option handler additionally to parameters of super class.

Method Summary

protected EuclideanDistanceFunction<V>	getDistanceFunction() Returns the distance function.
protected int	getK_i() Returns the value of K_I_PARAM.
protected int	getK() Returns the value of K_PARAM.
protected int	getL() Returns the value of L_PARAM..
Clustering<Model>	getResult() Retrieve the result.
List<String>	setParameters(List<String> args) Calls the super method and sets additionally the value of the parameters K_PARAM, K_I_PARAM, and L_PARAM.
protected void	setResult(Clustering<Model> result) Sets the result of this algorithm.

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

isTime, isVerbose, run, runInTime, setTime, setVerbose

Methods inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable

addOption, addParameterizable, addParameterizable, checkGlobalParameterConstraints, collectOptions, getAttributeSettings, getParameters, rememberParametersExcept, removeOption, removeParameterizable, shortDescription

Methods inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debugFine, debugFiner, debugFinest, exception, progress, verbose, warning

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

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.Algorithm

getDescription, setTime, setVerbose

Methods inherited from interface de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable

checkGlobalParameterConstraints, collectOptions, getParameters, shortDescription

Field Detail

K_ID

public static final OptionID K_ID

OptionID for K_PARAM

K_PARAM

private final IntParameter K_PARAM

Parameter to specify the number of clusters to find, must be an integer greater than 0.

Key: -projectedclustering.k

k

private int k

Holds the value of K_PARAM.

K_I_ID

public static final OptionID K_I_ID

OptionID for K_I_PARAM

K_I_PARAM

private final IntParameter K_I_PARAM

Parameter to specify the multiplier for the initial number of seeds, must be an integer greater than 0.

Default value: 30

Key: -projectedclustering.k_i

k_i

private int k_i

Holds the value of K_I_PARAM.

L_ID

public static final OptionID L_ID

OptionID for L_PARAM

L_PARAM

private final IntParameter L_PARAM

Parameter to specify the dimensionality of the clusters to find, must be an integer greater than 0.

Key: -projectedclustering.l

l

private int l

Holds the value of L_PARAM.

distanceFunction

private EuclideanDistanceFunction<V extends RealVector<V,?>> distanceFunction

The euclidean distance function.

result

private Clustering<Model> result

The result.

Constructor Detail

ProjectedClustering

public ProjectedClustering()

Adds parameters K_PARAM, K_I_PARAM, and L_PARAM to the option handler additionally to parameters of super class.

Method Detail

setParameters

public List<String> setParameters(List<String> args)
                           throws ParameterException

Calls the super method and sets additionally the value of the parameters K_PARAM, K_I_PARAM, and L_PARAM.

Specified by:

setParameters in interface Parameterizable

Overrides:

setParameters in class AbstractAlgorithm<V extends RealVector<V,?>,Clustering<Model>>

Parameters:

args - parameters to set the attributes accordingly to

Returns:

a list containing the unused parameters

Throws:

ParameterException - in case of wrong parameter-setting

getResult

public Clustering<Model> getResult()

Description copied from interface: ClusteringAlgorithm

Retrieve the result.

Specified by:

getResult in interface Algorithm<V extends RealVector<V,?>,Clustering<Model>>

Specified by:

getResult in interface ClusteringAlgorithm<Clustering<Model>,V extends RealVector<V,?>>

Returns:

the result of the algorithm

getDistanceFunction

protected EuclideanDistanceFunction<V> getDistanceFunction()

Returns the distance function.

Returns:

the distance function

getK

protected int getK()

Returns the value of K_PARAM.

Returns:

the number of clusters to be found

getK_i

protected int getK_i()

Returns the value of K_I_PARAM.

Returns:

the initial number of clusters

getL

protected int getL()

Returns the value of L_PARAM..

Returns:

the average dimensionality of the clusters to be found

setResult

protected void setResult(Clustering<Model> result)

Sets the result of this algorithm.

Parameters:

result - the result to be set