de.lmu.ifi.dbs.elki.algorithm.clustering.correlation
Class ERiC<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>
              de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC<V>

Type Parameters:

V - the type of Realvector handled by this Algorithm

All Implemented Interfaces:

Algorithm<V>, Loggable, Parameterizable

public class ERiC<V extends RealVector<V,?>>
extends AbstractAlgorithm<V>

Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result.

Author:

Elke Achtert

Field Summary

private COPAC<V>	copacAlgorithm The COPAC clustering algorithm.
private HierarchicalCorrelationClusters<V>	result Holds 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

Constructor Summary

ERiC()
Performs the COPAC algorithm on the data and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result.

Method Summary

private void	buildHierarchy(int dimensionality, SortedMap<Integer,List<HierarchicalCorrelationCluster<V>>> clusterMap)
String	description() Returns a description of the class and the required parameters.
private SortedMap<Integer,List<HierarchicalCorrelationCluster<V>>>	extractCorrelationClusters(Database<V> database, int dimensionality) Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension.
List<AttributeSettings>	getAttributeSettings() Returns the parameter setting of the attributes.
Description	getDescription() Returns a description of the algorithm.
Result<V>	getResult() Returns the result of the algorithm.
private boolean	isParent(ERiCDistanceFunction distanceFunction, HierarchicalCorrelationCluster<V> parent, List<HierarchicalCorrelationCluster<V>> children) Returns true, if the specified parent cluster is a parent of one child of the children clusters.
private String[]	pcaParameters(int correlationDimension) Returns the parameters for the PCA for the specified correlation dimension.
protected void	runInTime(Database<V> database) The run method encapsulated in measure of runtime.
String[]	setParameters(String[] args) Passes remaining parameters to the clustering algorithm.

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

isTime, isVerbose, run, setTime, setVerbose

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

addOption, checkGlobalParameterConstraints, deleteOption, description, description, getParameters, getParameterValue, getPossibleOptions, inlineDescription, isSet, setParameters

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

debugFine, debugFiner, debugFinest, exception, message, progress, progress, progress, verbose, 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.utilities.optionhandling.Parameterizable

checkGlobalParameterConstraints, getParameters, getPossibleOptions, inlineDescription

Field Detail

copacAlgorithm

private COPAC<V extends RealVector<V,?>> copacAlgorithm

The COPAC clustering algorithm.

result

private HierarchicalCorrelationClusters<V extends RealVector<V,?>> result

Holds the result.

Constructor Detail

ERiC

public ERiC()

Performs the COPAC algorithm on the data and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result.

Method Detail

runInTime

protected void runInTime(Database<V> database)
                  throws IllegalStateException

The run method encapsulated in measure of runtime. An extending class needs not to take care of runtime itself.

Specified by:

runInTime in class AbstractAlgorithm<V extends RealVector<V,?>>

Parameters:

database - the database to run the algorithm on

Throws:

IllegalStateException - if the algorithm has not been initialized properly (e.g. the setParameters(String[]) method has been failed to be called).

getResult

public Result<V> getResult()

Returns the result of the algorithm.

Returns:

the result of the algorithm

description

public String description()

Description copied from interface: Parameterizable

Returns a description of the class and the required parameters.

This description should be suitable for a usage description as for a standalone application.

Specified by:

description in interface Parameterizable

Overrides:

description in class AbstractAlgorithm<V extends RealVector<V,?>>

Returns:

String a description of the class and the required parameters

See Also:

Parameterizable.description()

getDescription

public Description getDescription()

Returns a description of the algorithm.

Returns:

a description of the algorithm

setParameters

public String[] setParameters(String[] args)
                       throws ParameterException

Passes remaining parameters to the clustering algorithm.

Specified by:

setParameters in interface Parameterizable

Overrides:

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

Parameters:

args - parameters to set the attributes accordingly to

Returns:

String[] an array containing the unused parameters

Throws:

ParameterException - in case of wrong parameter-setting

See Also:

Parameterizable.setParameters(String[])

getAttributeSettings

public List<AttributeSettings> getAttributeSettings()

Returns the parameter setting of the attributes.

Specified by:

getAttributeSettings in interface Parameterizable

Overrides:

getAttributeSettings in class AbstractParameterizable

Returns:

the parameter setting of the attributes

See Also:

Parameterizable.getAttributeSettings()

extractCorrelationClusters

private SortedMap<Integer,List<HierarchicalCorrelationCluster<V>>> extractCorrelationClusters(Database<V> database,
                                                                                              int dimensionality)

Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension. Each cluster is defined by the basis vectors defining the subspace in which the cluster appears.

Parameters:

database - the database containing the objects

dimensionality - the dimensionality of the feature space

Returns:

a mapping of correlation dimension to maps of clusters

pcaParameters

private String[] pcaParameters(int correlationDimension)

Returns the parameters for the PCA for the specified correlation dimension.

Parameters:

correlationDimension - the correlation dimension

Returns:

the parameters for the PCA for the specified correlation dimension

buildHierarchy

private void buildHierarchy(int dimensionality,
                            SortedMap<Integer,List<HierarchicalCorrelationCluster<V>>> clusterMap)

isParent

private boolean isParent(ERiCDistanceFunction distanceFunction,
                         HierarchicalCorrelationCluster<V> parent,
                         List<HierarchicalCorrelationCluster<V>> children)

Returns true, if the specified parent cluster is a parent of one child of the children clusters.

Parameters:

distanceFunction - the distance function for distance computation between the clusters

parent - the parent to be tested

children - the list of children to be tested

Returns:

true, if the specified parent cluster is a parent of one child of the children clusters, false otherwise