Environment for
DeveLoping
KDD-Applications
Supported by Index-Structures

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

java.lang.Object
  extended by de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      extended by de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<V,Clustering<Model>>
          extended by 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

Release 0.3 (2010-03-31_1612)