Package elki.outlier.intrinsic

Class LID<O>

  • Type Parameters:
    O - Object type
    All Implemented Interfaces:
    elki.Algorithm, OutlierAlgorithm
    @Title("LID: Intrinsic Dimensionality Outlier")
@Reference(authors="Michael E. Houle, Erich Schubert, Arthur Zimek",
           title="On the Correlation Between Local Intrinsic Dimensionality and Outlierness",
           booktitle="Proc. 11th Int. Conf. Similarity Search and Applications (SISAP\'2018)",
           url="https://doi.org/10.1007/978-3-030-02224-2_14",
           bibkey="DBLP:conf/sisap/HouleSZ18")
public class LID<O>
extends java.lang.Object
implements OutlierAlgorithm
    Use intrinsic dimensionality for outlier detection.

    Reference:

    Michael E. Houle, Erich Schubert, Arthur Zimek
    On the Correlation Between Local Intrinsic Dimensionality and Outlierness
    Proc. 11th Int. Conf. Similarity Search and Applications (SISAP'2018)

    This idea was also briefly explored before by Michael Houle, Arthur Zimek, Jonathan von Brünken, et al.

    Since:
    0.7.0
    Author:
    Erich Schubert

    • Nested Class Summary

      Nested Classes 
      Modifier and Type Class Description
      static class  LID.Par<O>
      Parameterization class.

      • 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 elki.math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator<? super O> estimator
      Estimator for intrinsic dimensionality.
      protected int kplus
      Number of neighbors to use + query point.
      private static elki.logging.Logging LOG
      Class logger.

    • Constructor Summary

      Constructors 
      Constructor Description
      LID​(elki.distance.Distance<? super O> distance, int k, elki.math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator<? super O> estimator)
      Constructor.

    • Field Detail

      • LOG

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

      • distance

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

      • kplus

        protected int kplus
        Number of neighbors to use + query point.

      • estimator

        protected elki.math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator<? super O> estimator
        Estimator for intrinsic dimensionality.

    • Constructor Detail

      • LID

        public LID​(elki.distance.Distance<? super O> distance,
           int k,
           elki.math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator<? super O> estimator)
        Constructor.
        Parameters:
        distance - Distance function
        k - Neighborhood size
        estimator - Estimator for intrinsic dimensionality

    • 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 algorithm
        Parameters:
        relation - Data relation
        Returns:
        Outlier result