Package ch.usi.inf.sape.hac.agglomeration

Class WeightedAverageLinkage

java.lang.Object
ch.usi.inf.sape.hac.agglomeration.WeightedAverageLinkage
All Implemented Interfaces:
AgglomerationMethod
public final class WeightedAverageLinkage extends 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 Details

    • WeightedAverageLinkage

      public WeightedAverageLinkage()

  • Method Details

    • 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 String toString()
      Overrides:
      toString in class Object