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

Interface Linkage

All Known Implementing Classes:

CentroidLinkage, CompleteLinkage, FlexibleBetaLinkage, GroupAverageLinkage, MedianLinkage, MinimumVarianceLinkage, SingleLinkage, WardLinkage, WeightedAverageLinkage

@Reference(authors="G. N. Lance, W. T. Williams",
           title="A general theory of classificatory sorting strategies 1. Hierarchical systems",
           booktitle="The Computer Journal 9.4",
           url="https://doi.org/10.1093/comjnl/9.4.373",
           bibkey="doi:10.1093/comjnl/9.4.373")
public interface Linkage

Abstract interface for implementing a new linkage method into hierarchical clustering.

Reference:

G. N. Lance, W. T. Williams
A general theory of classificatory sorting strategies
1. Hierarchical systems
The Computer Journal 9.4

Since:

0.6.0

Author:

Erich Schubert

Method Summary

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.
default double	initial(double d, boolean issquare) Initialization of the distance matrix.
default double	restore(double d, boolean issquare) Restore a distance to the original scale.

Method Detail

initial

default double initial(double d,
                       boolean issquare)

Initialization of the distance matrix.

Parameters:

d - Distance

issquare - Flag to indicate the input values are already squared

Returns:

Initial value

restore

default double restore(double d,
                       boolean issquare)

Restore a distance to the original scale.

Parameters:

d - Distance

issquare - Flag to indicate the input values were already squared

Returns:

Initial value

combine

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

Compute combined linkage for two clusters.

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