Package elki.clustering.hierarchical.extraction

Class HDBSCANHierarchyExtraction

java.lang.Object

elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<DendrogramModel>>

@Reference(authors="R. J. G. B. Campello, D. Moulavi, J. Sander",title="Density-Based Clustering Based on Hierarchical Density Estimates",booktitle="Pacific-Asia Conf. Advances in Knowledge Discovery and Data Mining (PAKDD)",url="https://doi.org/10.1007/978-3-642-37456-2_14",bibkey="DBLP:conf/pakdd/CampelloMS13") @Reference(authors="R. J. G. B. Campello, D. Moulavi, A. Zimek, J. Sander",title="Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection",booktitle="ACM Trans. Knowl. Discov. Data 10(1)",url="https://doi.org/10.1145/2733381",bibkey="DBLP:journals/tkdd/CampelloMZS15")
public class HDBSCANHierarchyExtraction
extends java.lang.Object
implements ClusteringAlgorithm<Clustering<DendrogramModel>>

Extraction of simplified cluster hierarchies, as proposed in HDBSCAN, and additionally also compute the GLOSH outlier scores.

In contrast to the authors top-down approach, we use a bottom-up approach based on the more efficient pointer representation introduced in SLINK.

In particular, it can also be used to extract a hierarchy from a hierarchical agglomerative clustering.

Reference:

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

R. J. G. B. Campello, D. Moulavi, A. Zimek, J. Sander
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection
ACM Trans. Knowl. Discov. Data 10(1)

Note: some of the code is rather complex because we delay the creation of one-element clusters to reduce garbage collection overhead.

Since:

0.7.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
protected class	HDBSCANHierarchyExtraction.Instance	Instance for a single data set.
static class	HDBSCANHierarchyExtraction.Par	Parameterization class.
private static class	HDBSCANHierarchyExtraction.TempCluster	Temporary cluster.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private HierarchicalClusteringAlgorithm	algorithm	Clustering algorithm to run to obtain the hierarchy.
private boolean	hierarchical	Return a hierarchical result.
private static Logging	LOG	Class logger.
private int	minClSize	Minimum cluster size.

Constructor Summary

Constructors

Constructor	Description
HDBSCANHierarchyExtraction(HierarchicalClusteringAlgorithm algorithm, int minClSize, boolean hierarchical)	Constructor.

Method Summary

Modifier and Type	Method	Description
Clustering<DendrogramModel>	autorun(Database database)	Try to auto-run the algorithm on a database by calling a method called run, with an optional Database first, and with data relations as specified by Algorithm.getInputTypeRestriction().
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.
Clustering<DendrogramModel>	run(ClusterMergeHistory merges)	Process an existing result.

Methods inherited from class java.lang.Object

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

Field Detail

LOG

private static final Logging LOG

Class logger.

minClSize

private int minClSize

Minimum cluster size.

algorithm

private HierarchicalClusteringAlgorithm algorithm

Clustering algorithm to run to obtain the hierarchy.

hierarchical

private boolean hierarchical

Return a hierarchical result.

Constructor Detail

HDBSCANHierarchyExtraction

public HDBSCANHierarchyExtraction(HierarchicalClusteringAlgorithm algorithm,
                                  int minClSize,
                                  boolean hierarchical)

Constructor.

Parameters:

algorithm - Algorithm to run

minClSize - Minimum cluster size

hierarchical - Produce a hierarchical result

Method Detail

autorun

public Clustering<DendrogramModel> autorun(Database database)

Description copied from interface: Algorithm

Try to auto-run the algorithm on a database by calling a method called run, with an optional Database first, and with data relations as specified by Algorithm.getInputTypeRestriction().

Specified by:

autorun in interface Algorithm

Specified by:

autorun in interface ClusteringAlgorithm<Clustering<DendrogramModel>>

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm

run

public Clustering<DendrogramModel> run(ClusterMergeHistory merges)

Process an existing result.

Parameters:

merges - Existing result in pointer representation.

Returns:

Clustering

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