Package tutorial.clustering

Class NaiveAgglomerativeHierarchicalClustering3<O>

java.lang.Object

tutorial.clustering.NaiveAgglomerativeHierarchicalClustering3<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm

@Reference(authors="R. M. Cormack",
           title="A Review of Classification",
           booktitle="Journal of the Royal Statistical Society. Series A, Vol. 134, No. 3",
           url="https://doi.org/10.2307/2344237",
           bibkey="doi:10.2307/2344237")
public class NaiveAgglomerativeHierarchicalClustering3<O>
extends java.lang.Object
implements Algorithm

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(n³) algorithm. See SLINK for a much faster algorithm (however, only for single-linkage).

Reference (for the update formulas):

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

Since:

0.6.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	NaiveAgglomerativeHierarchicalClustering3.Linkage	Different linkage strategies.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
(package private) Distance<? super O>	distance	Distance function used.
(package private) NaiveAgglomerativeHierarchicalClustering3.Linkage	linkage	Current linkage in use.
private static Logging	LOG	Class logger
(package private) int	numclusters	Threshold, how many clusters to extract.

Constructor Summary

Constructors

Constructor	Description
NaiveAgglomerativeHierarchicalClustering3(Distance<? super O> distance, int numclusters, NaiveAgglomerativeHierarchicalClustering3.Linkage linkage)	Constructor.

Method Summary

Modifier and Type	Method	Description
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.
Clustering<Model>	run(Relation<O> relation)	Perform HAC
protected static int	triangleSize(int x)	Compute the size of a complete x by x triangle (minus diagonal)

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.Algorithm

autorun

Field Detail

LOG

private static final Logging LOG

Class logger

distance

Distance<? super O> distance

Distance function used.

numclusters

int numclusters

Threshold, how many clusters to extract.

linkage

NaiveAgglomerativeHierarchicalClustering3.Linkage linkage

Current linkage in use.

Constructor Detail

NaiveAgglomerativeHierarchicalClustering3

public NaiveAgglomerativeHierarchicalClustering3(Distance<? super O> distance,
                                                 int numclusters,
                                                 NaiveAgglomerativeHierarchicalClustering3.Linkage linkage)

Constructor.

Parameters:

distance - Distance function to use

numclusters - Number of clusters

linkage - Linkage strategy

Method Detail

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

run

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

Perform HAC

Parameters:

relation - Data relation

Returns:

Clustering

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