AverageLinkage

java.lang.Object
- ch.usi.inf.sape.hac.agglomeration.AverageLinkage

All Implemented Interfaces:

AgglomerationMethod
```
public final class AverageLinkage
extends java.lang.Object
implements AgglomerationMethod
```
The "average", "group average", "unweighted average", or "Unweighted Pair Group Method using Arithmetic averages (UPGMA)", is a graph-based approach. The distance between two clusters is calculated as the average of the distances between all pairs of objects in opposite clusters. This method tends to produce small clusters of outliers, but does not deform the cluster space. [The data analysis handbook. By Ildiko E. Frank, Roberto Todeschini] The general form of the Lance-Williams matrix-update formula: d[(i,j),k] = ai*d[i,k] + aj*d[j,k] + b*d[i,j] + g*|d[i,k]-d[j,k]| For the "group average" method: ai = ci/(ci+cj) aj = cj/(ci+cj) b = 0 g = 0 Thus: d[(i,j),k] = ci/(ci+cj)*d[i,k] + cj/(ci+cj)*d[j,k] = ( ci*d[i,k] + cj*d[j,k] ) / (ci+cj)

Constructor Summary

Constructors
Constructor Description

AverageLinkage()

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`double`	`computeDissimilarity(double dik, double djk, double dij, int ci, int cj, int ck)`	Compute the dissimilarity between the newly formed cluster (i,j) and the existing cluster k.
`java.lang.String`	`toString()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - AverageLinkage
```
public AverageLinkage()
```
- Method Detail
  - computeDissimilarity
```
public double computeDissimilarity(double dik,
                                   double djk,
                                   double dij,
                                   int ci,
                                   int cj,
                                   int ck)
```
    Description copied from interface: AgglomerationMethod
    
    Compute the dissimilarity between the newly formed cluster (i,j) and the existing cluster k.
    
    Specified by:
    
    computeDissimilarity in interface AgglomerationMethod
    
    Parameters:
    
    dik - dissimilarity between clusters i and k
    
    djk - dissimilarity between clusters j and k
    
    dij - dissimilarity between clusters i and j
    
    ci - cardinality of cluster i
    
    cj - cardinality of cluster j
    
    ck - cardinality of cluster k
    
    Returns:
    
    dissimilarity between cluster (i,j) and cluster k.
  - toString
```
public java.lang.String toString()
```
    Overrides:
    
    toString in class java.lang.Object