Class SingleLinkage

  • All Implemented Interfaces:
    AgglomerationMethod

    public final class SingleLinkage
    extends java.lang.Object
    implements AgglomerationMethod
    The "single linkage", "minimum", "shortest distance", or "nearest neighbor" method is a graph-based approach. The distance between two clusters is calculated as the smallest distance between two objects in opposite clusters. This method tends to produce loosely bound large clusters with little internal cohesion. Linear, elongated clusters are formed as opposed to the more usual spherical clusters. This pheonomenon is called chaining. [The data analysis handbook. By Ildiko E. Frank, Roberto Todeschini] This method can cause "chaining" of clusters.
    • Constructor Summary

      Constructors 
      Constructor Description
      SingleLinkage()  
    • 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

      • SingleLinkage

        public SingleLinkage()
    • 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