Package elki.clustering.hierarchical

Class AGNES<O>

  • Type Parameters:
    O - Object type
    All Implemented Interfaces:
    elki.Algorithm, HierarchicalClusteringAlgorithm
    Direct Known Subclasses:
    Anderberg, NNChain
    @Reference(authors="L. Kaufman, P. J. Rousseeuw",title="Agglomerative Nesting (Program AGNES)",booktitle="Finding Groups in Data: An Introduction to Cluster Analysis",url="https://doi.org/10.1002/9780470316801.ch5",bibkey="doi:10.1002/9780470316801.ch5") @Reference(authors="P. H. Sneath",title="The application of computers to taxonomy",booktitle="Journal of general microbiology, 17(1)",url="https://doi.org/10.1099/00221287-17-1-201",bibkey="doi:10.1099/00221287-17-1-201") @Reference(authors="R. M. Cormack",title="A Review of Classification",booktitle="Journal of the Royal Statistical Society. Series A, Vol. 134, No. 3",url="https://doi.org/10.2307/2344237",bibkey="doi:10.2307/2344237")
@Alias({"HAC","SAHN"})
public class AGNES<O>
extends java.lang.Object
implements HierarchicalClusteringAlgorithm
    Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES) is a classic hierarchical clustering algorithm. Initially, each element is its own cluster; the closest clusters are merged at every step, until all the data has become a single cluster.

    This is the naive O(n³) algorithm. See SLINK for a much faster algorithm (however, only for single-linkage).

    This implementation uses the pointer-based representation used by SLINK, so that the extraction algorithms we have can be used with either of them.

    The algorithm is believed to be first published (for single-linkage) by:

    P. H. Sneath
    The application of computers to taxonomy
    Journal of general microbiology, 17(1).

    This algorithm is also known as AGNES (Agglomerative Nesting), where the use of alternative linkage criterions is discussed:

    L. Kaufman, P. J. Rousseeuw
    Agglomerative Nesting (Program AGNES),
    in Finding Groups in Data: An Introduction to Cluster Analysis

    Reference for the unified concept:

    G. N. Lance, W. T. Williams
    A general theory of classificatory sorting strategies 1. Hierarchical systems
    The computer journal 9.4 (1967): 373-380.

    See also:

    R. M. Cormack
    A Review of Classification
    Journal of the Royal Statistical Society. Series A, Vol. 134, No. 3

    Since:
    0.6.0
    Author:
    Erich Schubert

    • Nested Class Summary

      Nested Classes 
      Modifier and Type Class Description
      static class  AGNES.Instance
      Main worker instance of AGNES.

      • Nested classes/interfaces inherited from interface elki.Algorithm

        elki.Algorithm.Utils

    • Field Summary

      Fields 
      Modifier and Type Field Description
      protected elki.distance.Distance<? super O> distance
      Distance function used.
      protected Linkage linkage
      Current linkage method in use.
      private static elki.logging.Logging LOG
      Class logger

    • Constructor Summary

      Constructors 
      Constructor Description
      AGNES​(elki.distance.Distance<? super O> distance, Linkage linkage)
      Constructor.

    • Field Detail

      • LOG

        private static final elki.logging.Logging LOG
        Class logger

      • distance

        protected elki.distance.Distance<? super O> distance
        Distance function used.

      • linkage

        protected Linkage linkage
        Current linkage method in use.

    • Constructor Detail

      • AGNES

        public AGNES​(elki.distance.Distance<? super O> distance,
             Linkage linkage)
        Constructor.
        Parameters:
        distance - Distance function to use
        linkage - Linkage method

    • Method Detail

      • run

        public ClusterMergeHistory run​(elki.database.relation.Relation<O> relation)
        Run the algorithm
        Parameters:
        relation - Relation
        Returns:
        Clustering hierarchy

      • initializeDistanceMatrix

        protected static ClusterDistanceMatrix initializeDistanceMatrix​(elki.database.ids.ArrayDBIDs ids,
                                                                elki.database.query.distance.DistanceQuery<?> dq,
                                                                Linkage linkage)
        Initialize a distance matrix.
        Parameters:
        ids - Object ids
        dq - Distance query
        linkage - Linkage method
        Returns:
        cluster distance matrix

      • getInputTypeRestriction

        public elki.data.type.TypeInformation[] getInputTypeRestriction()
        Specified by:
        getInputTypeRestriction in interface elki.Algorithm