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

Class HDBSCANLinearMemory<O>

java.lang.Object

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

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

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AbstractHDBSCAN<O,PointerDensityHierarchyRepresentationResult>

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.HDBSCANLinearMemory<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, HierarchicalClusteringAlgorithm, DistanceBasedAlgorithm<O>

@Title(value="HDBSCAN: Hierarchical Density-Based Spatial Clustering of Applications with Noise")
@Description(value="Density-Based Clustering Based on Hierarchical Density Estimates")
@Reference(authors="R. J. G. B. Campello, D. Moulavi, and J. Sander",
           title="Density-Based Clustering Based on Hierarchical Density Estimates",
           booktitle="Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD",
           url="http://dx.doi.org/10.1007/978-3-642-37456-2_14")
public class HDBSCANLinearMemory<O>
extends AbstractHDBSCAN<O,PointerDensityHierarchyRepresentationResult>
implements HierarchicalClusteringAlgorithm

Linear memory implementation of HDBSCAN clustering. By not building a distance matrix, we can reduce memory usage to linear memory only; but at the cost of roughly double the runtime (unless using indexes) as we first need to compute all kNN distances (for core sizes), then recompute distances when building the spanning tree. This implementation follows the HDBSCAN publication more closely than SLINKHDBSCANLinearMemory, by computing the minimum spanning tree using Prim's algorithm (instead of SLINK; although the two are remarkably similar). In order to produce the preferred internal format of hierarchical clusterings (the compact pointer representation introduced in SLINK) we have to perform a postprocessing conversion. This implementation does not include the cluster extraction discussed as Step 4. This functionality should however already be provided by ExtractFlatClusteringFromHierarchy. For this reason, we also do not include self-edges. Reference:

R. J. G. B. Campello, D. Moulavi, and J. Sander
Density-Based Clustering Based on Hierarchical Density Estimates
Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	HDBSCANLinearMemory.Parameterizer<O> Parameterization class

Nested classes/interfaces inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AbstractHDBSCAN

AbstractHDBSCAN.HDBSCANAdapter, AbstractHDBSCAN.HeapMSTCollector

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG Class logger.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AbstractHDBSCAN

minPts

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
HDBSCANLinearMemory(DistanceFunction<? super O> distanceFunction, int minPts) 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.
PointerDensityHierarchyRepresentationResult	run(Database db, Relation<O> relation) Run the algorithm

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AbstractHDBSCAN

computeCoreDists, convertToPointerRepresentation

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.hierarchical.HierarchicalClusteringAlgorithm

run

Field Detail

LOG

private static final Logging LOG

Class logger.

Constructor Detail

HDBSCANLinearMemory

public HDBSCANLinearMemory(DistanceFunction<? super O> distanceFunction,
                   int minPts)

Constructor.

Parameters:

distanceFunction - Distance function

minPts - Minimum number of points for density

Method Detail

run

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

Run the algorithm

Parameters:

db - Database

relation - Relation

Returns:

Clustering hierarchy

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

Overrides:

getInputTypeRestriction in class AbstractHDBSCAN<O,PointerDensityHierarchyRepresentationResult>

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<PointerDensityHierarchyRepresentationResult>

Returns:

the static logger