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

Class NaiveAgglomerativeHierarchicalClustering<O,D extends NumberDistance<D,?>>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,D,PointerHierarchyRepresentationResult<DoubleDistance>>

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

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, HierarchicalClusteringAlgorithm<DoubleDistance>, DistanceBasedAlgorithm<O,D>, Parameterizable

@Reference(authors="G. N. Lance and W. T. Williams",
           title="A general theory of classificatory sorting strategies 1. Hierarchical systems",
           booktitle="The computer journal 9.4",
           url="http://dx.doi.org/ 10.1093/comjnl/9.4.373")
public class NaiveAgglomerativeHierarchicalClustering<O,D extends NumberDistance<D,?>>
extends AbstractDistanceBasedAlgorithm<O,D,PointerHierarchyRepresentationResult<DoubleDistance>>
implements HierarchicalClusteringAlgorithm<DoubleDistance>

This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps. This is the third step, where we add support for different linkage strategies. This is the naive O(n^3) algorithm. See SLINK for a much faster algorithm (however, only for single-linkage). Reference for the unified concept:

G. N. Lance and W. T. Williams
A general theory of classificatory sorting strategies 1. Hierarchical systems
The computer journal 9.4 (1967): 373-380.

See also:

A Review of Classification
R. M. Cormack
Journal of the Royal Statistical Society. Series A, Vol. 134, No. 3

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	NaiveAgglomerativeHierarchicalClustering.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class

Field Summary

Fields

Modifier and Type	Field and Description
(package private) LinkageMethod	linkage Current linkage method in use.
private static Logging	LOG Class logger

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
NaiveAgglomerativeHierarchicalClustering(DistanceFunction<? super O,D> distanceFunction, LinkageMethod linkage) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
DoubleDistance	getDistanceFactory() Return the distance type that will be used by the algorithm.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
PointerHierarchyRepresentationResult<DoubleDistance>	run(Database db, Relation<O> relation) Run the algorithm
protected static int	triangleSize(int x) Compute the size of a complete x by x triangle (minus diagonal)

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

linkage

LinkageMethod linkage

Current linkage method in use.

Constructor Detail

NaiveAgglomerativeHierarchicalClustering

public NaiveAgglomerativeHierarchicalClustering(DistanceFunction<? super O,D> distanceFunction,
                                        LinkageMethod linkage)

Constructor.

Parameters:

distanceFunction - Distance function to use

linkage - Linkage method

Method Detail

run

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

Run the algorithm

Parameters:

db - Database

relation - Relation

Returns:

Clustering hierarchy

triangleSize

protected static int triangleSize(int x)

Compute the size of a complete x by x triangle (minus diagonal)

Parameters:

x - Offset

Returns:

Size of complete triangle

getDistanceFactory

public DoubleDistance getDistanceFactory()

Description copied from interface: HierarchicalClusteringAlgorithm

Return the distance type that will be used by the algorithm.

Specified by:

getDistanceFactory in interface HierarchicalClusteringAlgorithm<DoubleDistance>

Returns:

Distance factory.

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<PointerHierarchyRepresentationResult<DoubleDistance>>

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<PointerHierarchyRepresentationResult<DoubleDistance>>

Returns:

the static logger