Package elki.clustering.subspace

Class HiSC

java.lang.Object

elki.clustering.subspace.HiSC

All Implemented Interfaces:

Algorithm, GeneralizedOPTICS, OPTICSTypeAlgorithm

@Title("Finding Hierarchies of Subspace Clusters")
@Description("Algorithm for detecting hierarchies of subspace clusters.")
@Reference(authors="Elke Achtert, Christian B\u00f6hm, Hans-Petre Kriegel, Peer Kr\u00f6ger, Ina M\u00fcller-Gorman, Arthur Zimek",
           title="Finding Hierarchies of Subspace Clusters",
           booktitle="Proc. 10th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD\'06)",
           url="https://doi.org/10.1007/11871637_42",
           bibkey="DBLP:conf/pkdd/AchtertBKKMZ06")
public class HiSC
extends java.lang.Object
implements GeneralizedOPTICS

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

Reference:

Elke Achtert, Christian Böhm, Hans-Peter Kriegel, Peer Kröger, Ina Müller-Gorman, Arthur Zimek
Finding Hierarchies of Subspace Clusters
Proc. 10th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'06)

Since:

0.1

Author:

Elke Achtert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
private class	HiSC.Instance	Algorithm instance for a single data set.
static class	HiSC.Par	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private double	alpha	Holds the maximum diversion allowed.
protected int	k	The number of nearest neighbors considered to determine the preference vector.
private static Logging	LOG	The logger for this class.

Constructor Summary

Constructors

Constructor	Description
HiSC(double alpha, int k)	Constructor.

Method Summary

Modifier and Type	Method	Description
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.
int	getMinPts()	Get the minpts value used.
ClusterOrder	run(Relation<? extends NumberVector> relation)	Run the HiSC algorithm
double	weightedDistance(NumberVector v1, NumberVector v2, long[] weightVector)	Computes the weighted distance between the two specified vectors according to the given preference vector.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface elki.clustering.optics.OPTICSTypeAlgorithm

autorun

Field Detail

LOG

private static final Logging LOG

The logger for this class.

alpha

private double alpha

Holds the maximum diversion allowed.

k

protected int k

The number of nearest neighbors considered to determine the preference vector.

Constructor Detail

HiSC

public HiSC(double alpha,
            int k)

Constructor.

Parameters:

alpha - Alpha

k - k

Method Detail

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from interface: Algorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Returns:

Type restriction

run

public ClusterOrder run(Relation<? extends NumberVector> relation)

Run the HiSC algorithm

Parameters:

relation - Data relation

Returns:

OPTICS cluster order

weightedDistance

public double weightedDistance(NumberVector v1,
                               NumberVector 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.

Specified by:

getMinPts in interface OPTICSTypeAlgorithm

Returns:

minpts value