Class MedoidLinkage<O>

  • Type Parameters:
    O - Object type
    All Implemented Interfaces:
    Algorithm, HierarchicalClusteringAlgorithm

    @Reference(authors="D. Herr, Q. Han, S. Lohmann, T. Ertl",title="Visual clutter reduction through hierarchy-based projection of high-dimensional labeled data",booktitle="Graphics Interface Conference",url="https://doi.org/10.20380/GI2016.14",bibkey="DBLP:conf/graphicsinterface/HerrHLE16") @Reference(authors="S. Miyamoto, Y. Kaizu, Y. Endo",title="Hierarchical and non-hierarchical medoid clustering using asymmetric similarity measures",booktitle="Soft Computing and Intelligent Systems (SCIS) and Int. Symp. Advanced Intelligent Systems (ISIS)",url="https://doi.org/10.1109/SCIS-ISIS.2016.0091",bibkey="DBLP:conf/scisisis/MiyamotoKE16")
    public class MedoidLinkage<O>
    extends java.lang.Object
    implements HierarchicalClusteringAlgorithm
    Medoid linkage uses the distance of medoids as criterion. The implementation is a simplified version of AGNES, as we do not have to update a distance matrix. This approach was invented at least twice. An approach more consistent with the ideas of optimum medoids is found in HACAM, which uses the loss of the resulting medoid after merging instead.

    References:

    D. Herr, Q. Han, S. Lohmann, T. Ertl
    Visual clutter reduction through hierarchy-based projection of high-dimensional labeled data
    Graphics Interface Conference

    S. Miyamoto, Y. Kaizu, Y. Endo
    Hierarchical and non-hierarchical medoid clustering using asymmetric similarity measures
    Soft Computing and Intelligent Systems (SCIS) and Int. Symp. Advanced Intelligent Systems (ISIS)

    Since:
    0.8.0
    Author:
    Erich Schubert
    • Field Detail

      • LOG

        private static final Logging LOG
        Class logger
      • distance

        protected Distance<? super O> distance
        The distance function to use.
    • Constructor Detail

      • MedoidLinkage

        public MedoidLinkage​(Distance<? super O> distance)
        Constructor.
        Parameters:
        distance - Distance function to use