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

Class DiSH<V extends NumberVector<?>>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DiSH<V>

Type Parameters:

V - the type of NumberVector handled by this Algorithm

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<SubspaceModel<V>>>, SubspaceClusteringAlgorithm<SubspaceModel<V>>, Parameterizable

@Title(value="DiSH: Detecting Subspace cluster Hierarchies")
@Description(value="Algorithm to find hierarchical correlation clusters in subspaces.")
@Reference(authors="E. Achtert, C. B\u00f6hm, H.-P. Kriegel, P. Kr\u00f6ger, I. M\u00fcller-Gorman, A. Zimek",
           title="Detection and Visualization of Subspace Cluster Hierarchies",
           booktitle="Proc. 12th International Conference on Database Systems for Advanced Applications (DASFAA), Bangkok, Thailand, 2007",
           url="http://dx.doi.org/10.1007/978-3-540-71703-4_15")
public class DiSH<V extends NumberVector<?>>
extends AbstractAlgorithm<Clustering<SubspaceModel<V>>>
implements SubspaceClusteringAlgorithm<SubspaceModel<V>>

Algorithm for detecting subspace hierarchies.

Reference:
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, I. Müller-Gorman, A. Zimek: Detection and Visualization of Subspace Cluster Hierarchies.
In Proc. 12th International Conference on Database Systems for Advanced Applications (DASFAA), Bangkok, Thailand, 2007.

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
private DiSHDistanceFunction	dishDistance The distance function we use
private double	epsilon Holds the value of EPSILON_ID.
static OptionID	EPSILON_ID Parameter that specifies the maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector, must be a double equal to or greater than 0.
private static Logging	LOG The logger for this class.
static OptionID	MU_ID Parameter that specifies the a minimum number of points as a smoothing factor to avoid the single-link-effect, must be an integer greater than 0.
private Collection<Pair<OptionID,Object>>	opticsAlgorithmParameters Parameters that were given to OPTICS

Constructor Summary

Constructors

Constructor and Description
DiSH(double epsilon, DiSHDistanceFunction dishDistance, Collection<Pair<OptionID,Object>> opticsAlgorithmParameters) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private void	buildHierarchy(Relation<V> database, DiSHDistanceFunction.Instance<V> distFunc, Clustering<SubspaceModel<V>> clustering, List<Cluster<SubspaceModel<V>>> clusters, int dimensionality) Builds the cluster hierarchy.
private void	checkClusters(Relation<V> database, DiSHDistanceFunction.Instance<V> distFunc, Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> clustersMap, int minpts) Removes the clusters with size < minpts from the cluster map and adds them to their parents.
private Clustering<SubspaceModel<V>>	computeClusters(Relation<V> database, ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder, DiSHDistanceFunction.Instance<V> distFunc) Computes the hierarchical clusters according to the cluster order.
private Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>>	extractClusters(Relation<V> database, DiSHDistanceFunction.Instance<V> distFunc, ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder) Extracts the clusters from the cluster order.
private Pair<BitSet,ArrayModifiableDBIDs>	findParent(Relation<V> database, DiSHDistanceFunction.Instance<V> distFunc, Pair<BitSet,ArrayModifiableDBIDs> child, Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> clustersMap) Returns the parent of the specified cluster
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(Relation<V> database, DiSHDistanceFunction.Instance<V> distFunc, Cluster<SubspaceModel<V>> parent, Hierarchy.Iter<Cluster<SubspaceModel<V>>> iter) Returns true, if the specified parent cluster is a parent of one child of the children clusters.
Clustering<SubspaceModel<V>>	run(Database database, Relation<V> relation) Performs the DiSH algorithm on the given database.
private List<Cluster<SubspaceModel<V>>>	sortClusters(Relation<V> database, Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> clustersMap) Returns a sorted list of the clusters w.r.t. the subspace dimensionality in descending order.

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.

EPSILON_ID

public static final OptionID EPSILON_ID

Parameter that specifies the maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector, must be a double equal to or greater than 0.

Default value: 0.001

Key: -dish.epsilon

MU_ID

public static final OptionID MU_ID

Parameter that specifies the a minimum number of points as a smoothing factor to avoid the single-link-effect, must be an integer greater than 0.

Default value: 1

Key: -dish.mu

epsilon

private double epsilon

Holds the value of EPSILON_ID.

dishDistance

private DiSHDistanceFunction dishDistance

The distance function we use

opticsAlgorithmParameters

private Collection<Pair<OptionID,Object>> opticsAlgorithmParameters

Parameters that were given to OPTICS

Constructor Detail

DiSH

public DiSH(double epsilon,
    DiSHDistanceFunction dishDistance,
    Collection<Pair<OptionID,Object>> opticsAlgorithmParameters)

Constructor.

Parameters:

epsilon - Epsilon value

dishDistance - Distance function

opticsAlgorithmParameters - OPTICS parameters

Method Detail

run

public Clustering<SubspaceModel<V>> run(Database database,
                               Relation<V> relation)

Performs the DiSH algorithm on the given database.

Parameters:

database - Database to process

relation - Relation to process

computeClusters

private Clustering<SubspaceModel<V>> computeClusters(Relation<V> database,
                                           ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder,
                                           DiSHDistanceFunction.Instance<V> distFunc)

Computes the hierarchical clusters according to the cluster order.

Parameters:

database - the database holding the objects

clusterOrder - the cluster order

distFunc - Distance function

extractClusters

private Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> extractClusters(Relation<V> database,
                                                                  DiSHDistanceFunction.Instance<V> distFunc,
                                                                  ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder)

Extracts the clusters from the cluster order.

Parameters:

database - the database storing the objects

distFunc - the distance function

clusterOrder - the cluster order to extract the clusters from

Returns:

the extracted clusters

sortClusters

private List<Cluster<SubspaceModel<V>>> sortClusters(Relation<V> database,
                                           Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> clustersMap)

Returns a sorted list of the clusters w.r.t. the subspace dimensionality in descending order.

Parameters:

database - the database storing the objects

clustersMap - the mapping of bits sets to clusters

Returns:

a sorted list of the clusters

checkClusters

private void checkClusters(Relation<V> database,
                 DiSHDistanceFunction.Instance<V> distFunc,
                 Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> clustersMap,
                 int minpts)

Removes the clusters with size < minpts from the cluster map and adds them to their parents.

Parameters:

database - the database storing the objects

distFunc - the distance function

clustersMap - the map containing the clusters

minpts - MinPts

findParent

private Pair<BitSet,ArrayModifiableDBIDs> findParent(Relation<V> database,
                                           DiSHDistanceFunction.Instance<V> distFunc,
                                           Pair<BitSet,ArrayModifiableDBIDs> child,
                                           Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> clustersMap)

Returns the parent of the specified cluster

Parameters:

database - the database storing the objects

distFunc - the distance function

child - the child to search the parent for

clustersMap - the map containing the clusters

Returns:

the parent of the specified cluster

buildHierarchy

private void buildHierarchy(Relation<V> database,
                  DiSHDistanceFunction.Instance<V> distFunc,
                  Clustering<SubspaceModel<V>> clustering,
                  List<Cluster<SubspaceModel<V>>> clusters,
                  int dimensionality)

Builds the cluster hierarchy.

Parameters:

distFunc - the distance function

clustering - Clustering we process

clusters - the sorted list of clusters

dimensionality - the dimensionality of the data

database - the database containing the data objects

isParent

private boolean isParent(Relation<V> database,
               DiSHDistanceFunction.Instance<V> distFunc,
               Cluster<SubspaceModel<V>> parent,
               Hierarchy.Iter<Cluster<SubspaceModel<V>>> iter)

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

Parameters:

database - the database containing the objects

distFunc - the distance function for distance computation between the clusters

parent - the parent to be tested

iter - 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<SubspaceModel<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<SubspaceModel<V extends NumberVector<?>>>>

Returns:

the static logger