Package elki.clustering

Class CanopyPreClustering<O>

java.lang.Object

elki.clustering.CanopyPreClustering<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<PrototypeModel<O>>>

@Reference(authors="A. McCallum, K. Nigam, L. H. Ungar",
           title="Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching",
           booktitle="Proc. 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining",
           url="https://doi.org/10.1145/347090.347123",
           bibkey="DBLP:conf/kdd/McCallumNU00")
public class CanopyPreClustering<O>
extends java.lang.Object
implements ClusteringAlgorithm<Clustering<PrototypeModel<O>>>

Canopy pre-clustering is a simple preprocessing step for clustering.

Reference:

A. McCallum, K. Nigam, L. H. Ungar
Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching
Proc. 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining

Since:

0.6.0

Author:

Erich Schubert

Nested Class Summary

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private Distance<? super O>	distance	Distance function used.
private static Logging	LOG	Class logger.
private double	t1	Threshold for inclusion
private double	t2	Threshold for removal

Constructor Summary

Constructors

Constructor	Description
CanopyPreClustering(Distance<? super O> distance, double t1, double t2)	Constructor.

Method Summary

Modifier and Type	Method	Description
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.
Clustering<PrototypeModel<O>>	run(Relation<O> relation)	Run the canopy clustering algorithm

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm

autorun

Field Detail

LOG

private static final Logging LOG

Class logger.

distance

private Distance<? super O> distance

Distance function used.

t1

private double t1

Threshold for inclusion

t2

private double t2

Threshold for removal

Constructor Detail

CanopyPreClustering

public CanopyPreClustering(Distance<? super O> distance,
                           double t1,
                           double t2)

Constructor.

Parameters:

distance - Distance function

t1 - Inclusion threshold

t2 - Exclusion threshold

Method Detail

run

public Clustering<PrototypeModel<O>> run(Relation<O> relation)

Run the canopy clustering algorithm

Parameters:

relation - Relation to process

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