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

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      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 FeatureVector 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 NumberVector<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.

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

debug, logger

Constructor Summary

ProjectedClustering(Parameterization config)
Constructor, adhering to Parameterizable

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..

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.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

setTime, setVerbose

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 NumberVector<V,?>> distanceFunction

The euclidean distance function.

Constructor Detail

ProjectedClustering

public ProjectedClustering(Parameterization config)

Constructor, adhering to Parameterizable

Parameters:

config - Parameterization

Method Detail

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