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

Class HiSC<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.clustering.optics.GeneralizedOPTICS<V,CorrelationClusterOrder>

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

Type Parameters:

V - the type of NumberVector handled by the algorithm

All Implemented Interfaces:

Algorithm, OPTICSTypeAlgorithm

@Title(value="Finding Hierarchies of Subspace Clusters")
@Description(value="Algorithm for detecting hierarchies of subspace clusters.")
@Reference(authors="E. Achtert, C. B\u00f6hm, H.-P. Kriegel, P. Kr\u00f6ger, I. M\u00fcller-Gorman, A. Zimek",
           title="Finding Hierarchies of Subspace Clusters",
           booktitle="Proc. 10th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD\'06), Berlin, Germany, 2006",
           url="http://www.dbs.ifi.lmu.de/Publikationen/Papers/PKDD06-HiSC.pdf")
public class HiSC<V extends NumberVector>
extends GeneralizedOPTICS<V,CorrelationClusterOrder>

Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.

Reference: E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, I. Müller-Gorman, A. Zimek
Finding Hierarchies of Subspace Clusters.
In: Proc. 10th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'06), Berlin, Germany, 2006.

Author:

Elke Achtert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
private class	HiSC.Instance Algorithm instance for a single data set.
static class	HiSC.Parameterizer<V extends NumberVector> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private double	alpha Holds the maximum diversion allowed.
private IndexFactory<V,HiSCPreferenceVectorIndex<NumberVector>>	indexfactory Factory to produce
private static Logging	LOG The logger for this class.

Constructor Summary

Constructors

Constructor and Description
HiSC(IndexFactory<V,HiSCPreferenceVectorIndex<NumberVector>> indexfactory, double epsilon) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
int	getMinPts() Get the minpts value used.
ClusterOrder	run(Database db, Relation<V> relation) Run OPTICS on the database.
double	weightedDistance(V v1, V v2, long[] weightVector) Computes the weighted distance between the two specified vectors according to the given preference vector.

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.optics.OPTICSTypeAlgorithm

run

Field Detail

LOG

private static final Logging LOG

The logger for this class.

indexfactory

private IndexFactory<V extends NumberVector,HiSCPreferenceVectorIndex<NumberVector>> indexfactory

Factory to produce

alpha

private double alpha

Holds the maximum diversion allowed.

Constructor Detail

HiSC

public HiSC(IndexFactory<V,HiSCPreferenceVectorIndex<NumberVector>> indexfactory,
    double epsilon)

Constructor.

Parameters:

indexfactory - HiSC index factory

Method Detail

run

public ClusterOrder run(Database db,
               Relation<V> relation)

Description copied from class: GeneralizedOPTICS

Run OPTICS on the database.

Specified by:

run in class GeneralizedOPTICS<V extends NumberVector,CorrelationClusterOrder>

Parameters:

db - Database

relation - Relation

Returns:

Result

weightedDistance

public double weightedDistance(V v1,
                      V v2,
                      long[] weightVector)

Computes the weighted distance between the two specified vectors according to the given preference vector.

Parameters:

v1 - the first vector

v2 - the second vector

weightVector - the preference vector

Returns:

the weighted distance between the two specified vectors according to the given preference vector

getMinPts

public int getMinPts()

Description copied from interface: OPTICSTypeAlgorithm

Get the minpts value used. Needed for OPTICS Xi.

Returns:

minpts value

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<CorrelationClusterOrder>

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<CorrelationClusterOrder>

Returns:

the static logger