Package elki.clustering.kmeans.initialization.betula

Class CFRandomlyChosen

  • @Reference(authors="Andreas Lang and Erich Schubert",
           title="BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees",
           booktitle="Information Systems",
           url="https://doi.org/10.1016/j.is.2021.101918",
           bibkey="DBLP:journals/is/LangS22")
public class CFRandomlyChosen
extends AbstractCFKMeansInitialization
    Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features. For normal k-means use RandomlyChosen.

    References:

    Andreas Lang and Erich Schubert
    BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees
    Information Systems

    Since:
    0.8.0
    Author:
    Andreas Lang

    • Nested Class Summary

      Nested Classes 
      Modifier and Type Class Description
      static class  CFRandomlyChosen.Par
      Parameterization class.

    • Constructor Summary

      Constructors 
      Constructor Description
      CFRandomlyChosen​(elki.utilities.random.RandomFactory rf)
      Constructor.

    • Method Summary

      All Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      double[][] chooseInitialMeans​(CFTree<?> tree, java.util.List<? extends ClusterFeature> cfs, int k)
      Build the initial models.

      • Methods inherited from class java.lang.Object

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

    • Constructor Detail

      • CFRandomlyChosen

        public CFRandomlyChosen​(elki.utilities.random.RandomFactory rf)
        Constructor.
        Parameters:
        rf - Random generator

    • Method Detail

      • chooseInitialMeans

        public double[][] chooseInitialMeans​(CFTree<?> tree,
                                     java.util.List<? extends ClusterFeature> cfs,
                                     int k)
        Description copied from class: AbstractCFKMeansInitialization
        Build the initial models.
        Specified by:
        chooseInitialMeans in class AbstractCFKMeansInitialization
        Parameters:
        tree - CF tree
        cfs - Cluster features of the tree (may be ignored for tree-based initializations, should be an array list for efficiency)
        k - Number of clusters.
        Returns:
        initial cluster means