Package elki.outlier.lof

Class LDF<O extends elki.data.NumberVector>

  • Type Parameters:
    O - the type of objects handled by this algorithm
    All Implemented Interfaces:
    elki.Algorithm, OutlierAlgorithm
    @Title("LDF: Outlier Detection with Kernel Density Functions")
@Reference(authors="L. J. Latecki, A. Lazarevic, D. Pokrajac",
           title="Outlier Detection with Kernel Density Functions",
           booktitle="Machine Learning and Data Mining in Pattern Recognition",
           url="https://doi.org/10.1007/978-3-540-73499-4_6",
           bibkey="DBLP:conf/mldm/LateckiLP07")
public class LDF<O extends elki.data.NumberVector>
extends java.lang.Object
implements OutlierAlgorithm
    Outlier Detection with Kernel Density Functions.

    A variation of LOF which uses kernel density estimation, but in contrast to SimpleKernelDensityLOF also uses the reachability concept of LOF.

    Reference:

    Outlier Detection with Kernel Density Functions
    L. J. Latecki, A. Lazarevic, D. Pokrajac
    Machine Learning and Data Mining in Pattern Recognition

    Since:
    0.5.5
    Author:
    Erich Schubert

    • Nested Class Summary

      • Nested classes/interfaces inherited from interface elki.Algorithm

        elki.Algorithm.Utils

    • Field Summary

      Fields 
      Modifier and Type Field Description
      protected double c
      Scaling constant, to limit value range to 1/c
      protected elki.distance.Distance<? super O> distance
      Distance function used.
      protected double h
      Bandwidth scaling factor.
      protected elki.math.statistics.kernelfunctions.KernelDensityFunction kernel
      Kernel density function
      protected int kplus
      Parameter k + 1 for the query point.
      private static elki.logging.Logging LOG
      The logger for this class.

    • Constructor Summary

      Constructors 
      Constructor Description
      LDF​(int k, elki.distance.Distance<? super O> distance, elki.math.statistics.kernelfunctions.KernelDensityFunction kernel, double h, double c)
      Constructor.

    • Field Detail

      • LOG

        private static final elki.logging.Logging LOG
        The logger for this class.

      • distance

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

      • kplus

        protected int kplus
        Parameter k + 1 for the query point.

      • h

        protected double h
        Bandwidth scaling factor.

      • c

        protected double c
        Scaling constant, to limit value range to 1/c

      • kernel

        protected elki.math.statistics.kernelfunctions.KernelDensityFunction kernel
        Kernel density function

    • Constructor Detail

      • LDF

        public LDF​(int k,
           elki.distance.Distance<? super O> distance,
           elki.math.statistics.kernelfunctions.KernelDensityFunction kernel,
           double h,
           double c)
        Constructor.
        Parameters:
        k - the value of k
        kernel - Kernel function
        h - Kernel bandwidth scaling
        c - Score scaling parameter

    • Method Detail

      • getInputTypeRestriction

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

      • run

        public OutlierResult run​(elki.database.relation.Relation<O> relation)
        Run the naive kernel density LOF algorithm.
        Parameters:
        relation - Data to process
        Returns:
        LOF outlier result