Package elki.clustering.hierarchical

Class SLINKHDBSCANLinearMemory<O>

java.lang.Object

elki.clustering.hierarchical.AbstractHDBSCAN<O>

elki.clustering.hierarchical.SLINKHDBSCANLinearMemory<O>

All Implemented Interfaces:

elki.Algorithm, HierarchicalClusteringAlgorithm

@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")
public class SLINKHDBSCANLinearMemory<O>
extends AbstractHDBSCAN<O>
implements HierarchicalClusteringAlgorithm

Linear memory implementation of HDBSCAN clustering based on SLINK.

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 version uses the SLINK algorithm to directly produce the pointer representation expected by the extraction methods. The SLINK algorithm is closely related to Prim's minimum spanning tree, but produces the more compact pointer representation instead of an edges list.

This implementation does not include the cluster extraction discussed as Step 4. This functionality should however already be provided by HDBSCANHierarchyExtraction. For this reason, we also do not include self-edges.

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)

Since:

0.7.0

Author:

Erich Schubert

Nested Class Summary

Nested classes/interfaces inherited from class elki.clustering.hierarchical.AbstractHDBSCAN

AbstractHDBSCAN.HDBSCANAdapter, AbstractHDBSCAN.HeapMSTCollector

Nested classes/interfaces inherited from interface elki.Algorithm

elki.Algorithm.Utils

Field Summary

Fields

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

Fields inherited from class elki.clustering.hierarchical.AbstractHDBSCAN

distance, minPts

Constructor Summary

Constructors

Constructor	Description
SLINKHDBSCANLinearMemory(elki.distance.Distance<? super O> distance, int minPts)	Constructor.

Method Summary

Modifier and Type	Method	Description
elki.data.type.TypeInformation[]	getInputTypeRestriction()
protected elki.logging.Logging	getLogger()	Get the (STATIC) logger for this class.
ClusterMergeHistory	run(elki.database.relation.Relation<O> relation)	Run the algorithm
private void	step2(elki.database.ids.DBIDRef id, elki.database.ids.DBIDs processedIDs, elki.database.query.distance.DistanceQuery<? super O> distQuery, elki.database.datastore.DoubleDataStore coredists, elki.database.datastore.WritableDoubleDataStore m)	Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.
private void	step3(elki.database.ids.DBIDRef id, elki.database.datastore.WritableDBIDDataStore pi, elki.database.datastore.WritableDoubleDataStore lambda, elki.database.ids.DBIDs processedIDs, elki.database.datastore.WritableDoubleDataStore m)	Third step: Determine the values for P and L
private void	step4(elki.database.ids.DBIDRef id, elki.database.datastore.WritableDBIDDataStore pi, elki.database.datastore.WritableDoubleDataStore lambda, elki.database.ids.DBIDs processedIDs)	Fourth step: Actualize the clusters if necessary

Methods inherited from class elki.clustering.hierarchical.AbstractHDBSCAN

computeCoreDists, convertToMergeList

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.hierarchical.HierarchicalClusteringAlgorithm

autorun

Field Detail

LOG

private static final elki.logging.Logging LOG

Class logger.

Constructor Detail

SLINKHDBSCANLinearMemory

public SLINKHDBSCANLinearMemory(elki.distance.Distance<? super O> distance,
                                int minPts)

Constructor.

Parameters:

distance - Distance function

minPts - Minimum number of points for coredists

Method Detail

getInputTypeRestriction

public elki.data.type.TypeInformation[] getInputTypeRestriction()

Specified by:

getInputTypeRestriction in interface elki.Algorithm

Overrides:

getInputTypeRestriction in class AbstractHDBSCAN<O>

run

public ClusterMergeHistory run(elki.database.relation.Relation<O> relation)

Run the algorithm

Parameters:

relation - Relation

Returns:

Clustering hierarchy

step2

private void step2(elki.database.ids.DBIDRef id,
                   elki.database.ids.DBIDs processedIDs,
                   elki.database.query.distance.DistanceQuery<? super O> distQuery,
                   elki.database.datastore.DoubleDataStore coredists,
                   elki.database.datastore.WritableDoubleDataStore m)

Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.

Parameters:

id - the id of the object to be inserted into the pointer representation

processedIDs - the already processed ids

distQuery - Distance query

m - Data store

step3

private void step3(elki.database.ids.DBIDRef id,
                   elki.database.datastore.WritableDBIDDataStore pi,
                   elki.database.datastore.WritableDoubleDataStore lambda,
                   elki.database.ids.DBIDs processedIDs,
                   elki.database.datastore.WritableDoubleDataStore m)

Third step: Determine the values for P and L

Parameters:

id - the id of the object to be inserted into the pointer representation

pi - Pi data store

lambda - Lambda data store

processedIDs - the already processed ids

m - Data store

step4

private void step4(elki.database.ids.DBIDRef id,
                   elki.database.datastore.WritableDBIDDataStore pi,
                   elki.database.datastore.WritableDoubleDataStore lambda,
                   elki.database.ids.DBIDs processedIDs)

Fourth step: Actualize the clusters if necessary

Parameters:

id - the id of the current object

pi - Pi data store

lambda - Lambda data store

processedIDs - the already processed ids

getLogger

protected elki.logging.Logging getLogger()

Description copied from class: AbstractHDBSCAN

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractHDBSCAN<O>

Returns:

the static logger