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

Class ERiC<V extends NumberVector<?>>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<Clustering<CorrelationModel<V>>>

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

Type Parameters:

V - the type of NumberVector handled by this Algorithm

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<CorrelationModel<V>>>, InspectionUtilFrequentlyScanned, Parameterizable

@Title(value="ERiC: Exploring Relationships among Correlation Clusters")
@Description(value="Performs the DBSCAN algorithm on the data using a special distance function taking into account correlations among attributes and builds a hierarchy that allows multiple inheritance from the correlation clustering result.")
@Reference(authors="E. Achtert, C. B\u00f6hm, H.-P. Kriegel, P. Kr\u00f6ger, and A. Zimek",
           title="On Exploring Complex Relationships of Correlation Clusters",
           booktitle="Proc. 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007), Banff, Canada, 2007",
           url="http://dx.doi.org/10.1109/SSDBM.2007.21")
public class ERiC<V extends NumberVector<?>>
extends AbstractAlgorithm<Clustering<CorrelationModel<V>>>
implements ClusteringAlgorithm<Clustering<CorrelationModel<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.

Reference: E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, and A. Zimek: On Exploring Complex Relationships of Correlation Clusters.
In Proc. 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007), Banff, Canada, 2007.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ERiC.Parameterizer<V extends NumberVector<?>> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private COPAC<V,IntegerDistance>	copacAlgorithm The COPAC clustering algorithm.
private static Logging	LOG The logger for this class.

Constructor Summary

Constructors

Constructor and Description
ERiC(COPAC<V,IntegerDistance> copacAlgorithm) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private void	buildHierarchy(SortedMap<Integer,List<Cluster<CorrelationModel<V>>>> clusterMap, DistanceQuery<V,IntegerDistance> query)
private SortedMap<Integer,List<Cluster<CorrelationModel<V>>>>	extractCorrelationClusters(Clustering<Model> copacResult, Relation<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.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
private boolean	isParent(ERiCDistanceFunction distanceFunction, Cluster<CorrelationModel<V>> parent, List<Cluster<CorrelationModel<V>>> children) Returns true, if the specified parent cluster is a parent of one child of the children clusters.
private ListParameterization	pcaParameters(int correlationDimension) Returns the parameters for the PCA for the specified correlation dimension.
Clustering<CorrelationModel<V>>	run(Relation<V> relation) Performs the ERiC algorithm on the given database.

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

The logger for this class.

copacAlgorithm

private COPAC<V extends NumberVector<?>,IntegerDistance> copacAlgorithm

The COPAC clustering algorithm.

Constructor Detail

ERiC

public ERiC(COPAC<V,IntegerDistance> copacAlgorithm)

Constructor.

Parameters:

copacAlgorithm - COPAC to use

Method Detail

run

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

Performs the ERiC algorithm on the given database.

Parameters:

relation - Relation to process

Returns:

Clustering result

extractCorrelationClusters

private SortedMap<Integer,List<Cluster<CorrelationModel<V>>>> extractCorrelationClusters(Clustering<Model> copacResult,
                                                                               Relation<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:

copacResult -

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 ListParameterization 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(SortedMap<Integer,List<Cluster<CorrelationModel<V>>>> clusterMap,
                  DistanceQuery<V,IntegerDistance> query)

isParent

private boolean isParent(ERiCDistanceFunction distanceFunction,
               Cluster<CorrelationModel<V>> parent,
               List<Cluster<CorrelationModel<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

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<CorrelationModel<V extends NumberVector<?>>>>

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<CorrelationModel<V extends NumberVector<?>>>>

Returns:

the static logger