Package elki.utilities.scaling.outlier

Class OutlierGammaScaling

  • All Implemented Interfaces:
    OutlierScaling, ScalingFunction
    Direct Known Subclasses:
    MinusLogGammaScaling
    @Reference(authors="Hans-Peter Kriegel, Peer Kr\u00f6ger, Erich Schubert, Arthur Zimek",
           title="Interpreting and Unifying Outlier Scores",
           booktitle="Proc. 11th SIAM International Conference on Data Mining (SDM 2011)",
           url="https://doi.org/10.1137/1.9781611972818.2",
           bibkey="DBLP:conf/sdm/KriegelKSZ11")
public class OutlierGammaScaling
extends java.lang.Object
implements OutlierScaling
    Scaling that can map arbitrary values to a probability in the range of [0:1] by assuming a Gamma distribution on the values.

    Reference:

    Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
    Interpreting and Unifying Outlier Scores
    Proc. 11th SIAM International Conference on Data Mining (SDM 2011)

    Since:
    0.3
    Author:
    Erich Schubert

    • Nested Class Summary

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

    • Field Summary

      Fields 
      Modifier and Type Field Description
      (package private) double atmean
      Score at the mean, for cut-off.
      (package private) double k
      Gamma parameter k
      (package private) OutlierScoreMeta meta
      Keep a reference to the outlier score meta, for normalization.
      (package private) boolean normalize
      Store flag to Normalize data before curve fitting.
      (package private) double theta
      Gamma parameter theta

    • Constructor Summary

      Constructors 
      Constructor Description
      OutlierGammaScaling​(boolean normalize)
      Constructor.

    • Method Summary

      All Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      double getMax()
      Get maximum resulting value.
      double getMin()
      Get minimum resulting value.
      double getScaled​(double value)
      Transform a given value using the scaling function.
      <A> void prepare​(A array, NumberArrayAdapter<?,​A> adapter)
      Prepare is called once for each data set, before getScaled() will be called.
      void prepare​(OutlierResult or)
      Prepare is called once for each data set, before getScaled() will be called.
      protected double preScale​(double score)
      Normalize data if necessary.

      • Methods inherited from class java.lang.Object

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

    • Field Detail

      • k

        double k
        Gamma parameter k

      • theta

        double theta
        Gamma parameter theta

      • atmean

        double atmean
        Score at the mean, for cut-off.

      • normalize

        boolean normalize
        Store flag to Normalize data before curve fitting.

      • meta

        OutlierScoreMeta meta
        Keep a reference to the outlier score meta, for normalization.

    • Constructor Detail

      • OutlierGammaScaling

        public OutlierGammaScaling​(boolean normalize)
        Constructor.
        Parameters:
        normalize - Normalization flag

    • Method Detail

      • getScaled

        public double getScaled​(double value)
        Description copied from interface: ScalingFunction
        Transform a given value using the scaling function.
        Specified by:
        getScaled in interface ScalingFunction
        Parameters:
        value - Original value
        Returns:
        Scaled value

      • prepare

        public void prepare​(OutlierResult or)
        Description copied from interface: OutlierScaling
        Prepare is called once for each data set, before getScaled() will be called. This function can be used to extract global parameters such as means, minimums or maximums from the outlier scores.
        Specified by:
        prepare in interface OutlierScaling
        Parameters:
        or - Outlier result to use

      • prepare

        public <A> void prepare​(A array,
                        NumberArrayAdapter<?,​A> adapter)
        Description copied from interface: OutlierScaling
        Prepare is called once for each data set, before getScaled() will be called. This function can be used to extract global parameters such as means, minimums or maximums from the score array. The method using a full OutlierResult is preferred, as it will allow access to the metadata.
        Specified by:
        prepare in interface OutlierScaling
        Parameters:
        array - Data to process
        adapter - Array adapter

      • preScale

        protected double preScale​(double score)
        Normalize data if necessary.

        Note: this is overridden by MinusLogGammaScaling!

        Parameters:
        score - Original score
        Returns:
        Normalized score.

      • getMin

        public double getMin()
        Description copied from interface: ScalingFunction
        Get minimum resulting value. May be Double.NaN or Double.NEGATIVE_INFINITY.
        Specified by:
        getMin in interface ScalingFunction
        Returns:
        Minimum resulting value.

      • getMax

        public double getMax()
        Description copied from interface: ScalingFunction
        Get maximum resulting value. May be Double.NaN or Double.POSITIVE_INFINITY.
        Specified by:
        getMax in interface ScalingFunction
        Returns:
        Maximum resulting value.