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

Class OPTICSHeap<O>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,ClusterOrder>

de.lmu.ifi.dbs.elki.algorithm.clustering.optics.AbstractOPTICS<O>

de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSHeap<O>

Type Parameters:

O - the type of DatabaseObjects handled by the algorithm

All Implemented Interfaces:

Algorithm, OPTICSTypeAlgorithm, DistanceBasedAlgorithm<O>

@Title(value="OPTICS: Density-Based Hierarchical Clustering")
@Description(value="Algorithm to find density-connected sets in a database based on the parameters \'minPts\' and \'epsilon\' (specifying a volume). These two parameters determine a density threshold for clustering.")
@Reference(authors="M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander",
           title="OPTICS: Ordering Points to Identify the Clustering Structure",
           booktitle="Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD \'99)",
           url="http://dx.doi.org/10.1145/304181.304187")
@Alias(value={"OPTICS","de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICS","de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICS"})
public class OPTICSHeap<O>
extends AbstractOPTICS<O>

The OPTICS algorithm for density-based hierarchical clustering. This implementation uses a heap. Reference:

M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander:
OPTICS: Ordering Points to Identify the Clustering Structure.
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '99).

Author:

Elke Achtert, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
private class	OPTICSHeap.Instance Instance for processing a single data set.
static class	OPTICSHeap.Parameterizer<O> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG The logger for this class.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.optics.AbstractOPTICS

epsilon, minpts

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
OPTICSHeap(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
protected Logging	getLogger() Get the (STATIC) logger for this class.
ClusterOrder	run(Database db, Relation<O> relation) Run OPTICS on the database.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.optics.AbstractOPTICS

getInputTypeRestriction, getMinPts

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

getDistanceFunction

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.

Constructor Detail

OPTICSHeap

public OPTICSHeap(DistanceFunction<? super O> distanceFunction,
          double epsilon,
          int minpts)

Constructor.

Parameters:

distanceFunction - Distance function

epsilon - Epsilon value

minpts - Minpts value

Method Detail

run

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

Description copied from class: AbstractOPTICS

Run OPTICS on the database.

Specified by:

run in class AbstractOPTICS<O>

Parameters:

db - Database

relation - Relation

Returns:

Result

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<ClusterOrder>

Returns:

the static logger