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

Class CanopyPreClustering<O,D extends Distance<D>>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,D,Clustering<ClusterModel>>

de.lmu.ifi.dbs.elki.algorithm.clustering.CanopyPreClustering<O,D>

Type Parameters:

O - Object type

D - Distance type

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<ClusterModel>>, DistanceBasedAlgorithm<O,D>, Parameterizable

@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 international conference on Knowledge discovery and data mining",
           url="http://dx.doi.org/10.1145%2F347090.347123")
public class CanopyPreClustering<O,D extends Distance<D>>
extends AbstractDistanceBasedAlgorithm<O,D,Clustering<ClusterModel>>
implements ClusteringAlgorithm<Clustering<ClusterModel>>

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 international conference on Knowledge discovery and data mining

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	CanopyPreClustering.Parameterizer<O,D extends Distance<D>> Parameterization class

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG Class logger.
private D	t1 Threshold for inclusion
private D	t2 Threshold for removal

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
CanopyPreClustering(DistanceFunction<? super O,D> distanceFunction, D t1, D t2) 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.
Clustering<ClusterModel>	run(Database database, Relation<O> relation) Run the algorithm

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.ClusteringAlgorithm

run

Field Detail

LOG

private static final Logging LOG

Class logger.

t1

private D extends Distance<D> t1

Threshold for inclusion

t2

private D extends Distance<D> t2

Threshold for removal

Constructor Detail

CanopyPreClustering

public CanopyPreClustering(DistanceFunction<? super O,D> distanceFunction,
                   D t1,
                   D t2)

Constructor.

Parameters:

distanceFunction - Distance function

t1 - Inclusion threshold

t2 - Exclusion threshold

Method Detail

run

public Clustering<ClusterModel> run(Database database,
                           Relation<O> relation)

Run the algorithm

Parameters:

database - Database

relation - Relation to process

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

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<Clustering<ClusterModel>>

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<Clustering<ClusterModel>>

Returns:

the static logger