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

Class CentroidLinkageMethod

java.lang.Object

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

All Implemented Interfaces:

LinkageMethod

@Alias(value={"centroid","upgmc"})
@Reference(authors="A. K. Jain and R. C. Dubes",
           title="Algorithms for Clustering Data",
           booktitle="Algorithms for Clustering Data, Prentice-Hall")
public class CentroidLinkageMethod
extends Object
implements LinkageMethod

Centroid linkage clustering method, aka UPGMC: Unweighted Pair-Group Method using Centroids. Reference:

A. K. Jain and R. C. Dubes
Algorithms for Clustering Data
Prentice-Hall

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	CentroidLinkageMethod.Parameterizer Class parameterizer.

Field Summary

Fields

Modifier and Type	Field and Description
static CentroidLinkageMethod	STATIC Static instance of class.

Constructor Summary

Constructors

Constructor and Description
CentroidLinkageMethod() Deprecated. use the static instance STATIC instead.

Method Summary

Methods

Modifier and Type	Method and Description
double	combine(int sizex, double dx, int sizey, double dy, int sizej, double dxy) Compute combined linkage for two clusters.

Methods inherited from class java.lang.Object

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

Field Detail

STATIC

public static final CentroidLinkageMethod STATIC

Static instance of class.

Constructor Detail

CentroidLinkageMethod

@Deprecated
public CentroidLinkageMethod()

Deprecated. use the static instance STATIC instead.

Constructor.

Method Detail

combine

public double combine(int sizex,
             double dx,
             int sizey,
             double dy,
             int sizej,
             double dxy)

Description copied from interface: LinkageMethod

Compute combined linkage for two clusters.

Specified by:

combine in interface LinkageMethod

Parameters:

sizex - Size of first cluster x before merging

dx - Distance of cluster x to j before merging

sizey - Size of second cluster y before merging

dy - Distance of cluster y to j before merging

sizej - Size of candidate cluster j

dxy - Distance between clusters x and y before merging

Returns:

Combined distance