tutorial.clustering

Class NaiveAgglomerativeHierarchicalClustering4<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>>

tutorial.clustering.NaiveAgglomerativeHierarchicalClustering4<O,D>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, HierarchicalClusteringAlgorithm<DoubleDistance>, Parameterizable

@Reference(title="A Review of Classification",
           authors="R. M. Cormack",
           booktitle="Journal of the Royal Statistical Society. Series A, Vol. 134, No. 3",
           url="http://www.jstor.org/stable/2344237")
public class NaiveAgglomerativeHierarchicalClustering4<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 update formulas):

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	NaiveAgglomerativeHierarchicalClustering4.Linkage Different linkage strategies.
static class	NaiveAgglomerativeHierarchicalClustering4.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class

Field Summary

Fields

Modifier and Type	Field and Description
(package private) NaiveAgglomerativeHierarchicalClustering4.Linkage	linkage Current linkage in use.
private static Logging	LOG Class logger

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
NaiveAgglomerativeHierarchicalClustering4(DistanceFunction<? super O,D> distanceFunction, NaiveAgglomerativeHierarchicalClustering4.Linkage 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

NaiveAgglomerativeHierarchicalClustering4.Linkage linkage

Current linkage in use.

Constructor Detail

NaiveAgglomerativeHierarchicalClustering4

public NaiveAgglomerativeHierarchicalClustering4(DistanceFunction<? super O,D> distanceFunction,
                                         NaiveAgglomerativeHierarchicalClustering4.Linkage linkage)

Constructor.

Parameters:

distanceFunction - Distance function to use

linkage - Linkage strategy

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

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

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.