Package elki.clustering.hierarchical.birch

Class BIRCHKMeansPlusPlus

  • public class BIRCHKMeansPlusPlus
extends java.lang.Object
    K-Means++-like initialization for BIRCH k-means; this cannot be used to initialize regular k-means, use KMeansPlusPlus instead.
    Since:
    0.8.0
    Author:
    Andreas Lang

    • Nested Class Summary

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

    • Field Summary

      Fields 
      Modifier and Type Field Description
      protected RandomFactory random
      Random generator
      protected double[] weights
      Weights

    • Method Summary

      All Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      private double distance​(double[] ds, double[] ds2)
      Calculates distance between two vectors.
      private double initialWeights​(double[] first, double[][] x)
      Initialize the weight list.
      double[][] run​(double[][] x, int k)
      Perform k-means++ initialization.
      private double updateWeights​(double[] latest, double[][] x)
      Update the weight list.

      • Methods inherited from class java.lang.Object

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

    • Field Detail

      • weights

        protected double[] weights
        Weights

    • Constructor Detail

      • BIRCHKMeansPlusPlus

        public BIRCHKMeansPlusPlus​(RandomFactory rnd)
        Constructor.
        Parameters:
        rnd - Random generator.

    • Method Detail

      • run

        public double[][] run​(double[][] x,
                      int k)
        Perform k-means++ initialization.
        Parameters:
        x - Input vectors.
        k - K
        Returns:
        Initial cluster centers

      • initialWeights

        private double initialWeights​(double[] first,
                              double[][] x)
        Initialize the weight list.
        Parameters:
        first - Id of first mean.
        x - Input data.
        Returns:
        Sum of weights

      • distance

        private double distance​(double[] ds,
                        double[] ds2)
        Calculates distance between two vectors.
        Parameters:
        ds - First Vector
        ds2 - Second Vector
        Returns:
        Vector

      • updateWeights

        private double updateWeights​(double[] latest,
                             double[][] x)
        Update the weight list.
        Parameters:
        latest - Latest center
        Returns:
        Weight sum