Package elki.clustering.kmedoids.initialization

Class BUILD<O>

  • Type Parameters:
    O - Object type for KMedoids initialization
    All Implemented Interfaces:
    KMeansInitialization, KMedoidsInitialization<O>
    @Reference(authors="L. Kaufman, P. J. Rousseeuw",title="Clustering by means of Medoids",booktitle="Statistical Data Analysis Based on the L1-Norm and Related Methods",bibkey="books/misc/KauRou87") @Reference(authors="L. Kaufman, P. J. Rousseeuw",title="Partitioning Around Medoids (Program PAM)",booktitle="Finding Groups in Data: An Introduction to Cluster Analysis",url="https://doi.org/10.1002/9780470316801.ch2",bibkey="doi:10.1002/9780470316801.ch2") @Reference(authors="R. A. Whitaker",title="A Fast Algorithm For The Greedy Interchange For Large-Scale Clustering And Median Location Problems",booktitle="INFOR: Information Systems and Operational Research 21(2)",url="https://doi.org/10.1080/03155986.1983.11731889",bibkey="doi:10.1080/03155986.1983.11731889")
public class BUILD<O>
extends java.lang.Object
implements KMeansInitialization, KMedoidsInitialization<O>
    PAM initialization for k-means (and of course, for PAM). While this version is based on the work of Kaufman and Rousseeuw, a similar approach was also proposed by Whitaker in the operations research domain, and is well known by the name "fast greedy" there.

    Reference:

    L. Kaufman, P. J. Rousseeuw
    Clustering by means of Medoids
    in: Statistical Data Analysis Based on the L1-Norm and Related Methods

    R. A. Whitaker
    A Fast Algorithm For The Greedy Interchange For Large-Scale Clustering And Median Location Problems
    INFOR: Information Systems and Operational Research 21(2)

    Since:
    0.5.0
    Author:
    Erich Schubert

    • Nested Class Summary

      Nested Classes 
      Modifier and Type Class Description
      static class  BUILD.Par<V>
      Parameterization class.

    • Field Summary

      Fields 
      Modifier and Type Field Description
      private static elki.logging.Logging LOG
      Class logger.

    • Constructor Summary

      Constructors 
      Constructor Description
      BUILD()
      Constructor.

    • Method Summary

      All Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      double[][] chooseInitialMeans​(elki.database.relation.Relation<? extends elki.data.NumberVector> relation, int k, elki.distance.NumberVectorDistance<?> distance)
      Choose initial means
      elki.database.ids.DBIDs chooseInitialMedoids​(int k, elki.database.ids.DBIDs ids, elki.database.query.distance.DistanceQuery<? super O> distQ)
      Choose initial means

      • Methods inherited from class java.lang.Object

        clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Field Detail

      • LOG

        private static final elki.logging.Logging LOG
        Class logger.

    • Constructor Detail

      • BUILD

        public BUILD()
        Constructor.

    • Method Detail

      • chooseInitialMeans

        public double[][] chooseInitialMeans​(elki.database.relation.Relation<? extends elki.data.NumberVector> relation,
                                     int k,
                                     elki.distance.NumberVectorDistance<?> distance)
        Description copied from interface: KMeansInitialization
        Choose initial means
        Specified by:
        chooseInitialMeans in interface KMeansInitialization
        Parameters:
        relation - Relation
        k - Parameter k
        distance - Distance function
        Returns:
        List of chosen means for k-means

      • chooseInitialMedoids

        public elki.database.ids.DBIDs chooseInitialMedoids​(int k,
                                                    elki.database.ids.DBIDs ids,
                                                    elki.database.query.distance.DistanceQuery<? super O> distQ)
        Description copied from interface: KMedoidsInitialization
        Choose initial means
        Specified by:
        chooseInitialMedoids in interface KMedoidsInitialization<O>
        Parameters:
        k - Parameter k
        ids - Candidate IDs.
        distQ - Distance function
        Returns:
        List of chosen means for k-means