Class CompleteLinkage

  • All Implemented Interfaces:
    AgglomerationMethod

    public final class CompleteLinkage
    extends java.lang.Object
    implements AgglomerationMethod
    The "complete", "maximum", "clique", "furthest neighbor", or "furthest distance" method is a graph-based approach. The distance between two clusters is calculated as the largest distance between two objects in opposite clusters. This method tends to produce well separated, small, compact spherical clusters. The cluster space is dilated. [The data analysis handbook. By Ildiko E. Frank, Roberto Todeschini] This method tends to produce compact clusters. Outliers are given more weight with this method. It is generally a good choice if the clusters are far apart in feature space, but not good if the data are noisy.
    • Constructor Summary

      Constructors 
      Constructor Description
      CompleteLinkage()  
    • 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

      • CompleteLinkage

        public CompleteLinkage()
    • 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