WeightedAverageLinkage

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

All Implemented Interfaces:

AgglomerationMethod
```
public final class WeightedAverageLinkage
extends java.lang.Object
implements AgglomerationMethod
```
The "weighted average", "McQuitty", or "Weighted Pair-Group Method using Arithmetic averages, or WPGMA)" method. Average linkage where the sizes of the clusters are assumed to be equal. This method, similar to "Median", weights small and large clusters equally. [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 "McQuitty" method: ai = 0.5 aj = 0.5 b = 0 g = 0 Thus: d[(i,j),k] = 0.5*d[i,k] + 0.5*d[j,k]

Constructor Summary

Constructors
Constructor Description

WeightedAverageLinkage()

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
  - WeightedAverageLinkage
```
public WeightedAverageLinkage()
```
- 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