Class MinusLogGammaScaling

  • All Implemented Interfaces:
    OutlierScaling, ScalingFunction

    @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 MinusLogGammaScaling
    extends OutlierGammaScaling
    Scaling that can map arbitrary values to a probability in the range of [0:1], by assuming a Gamma distribution on the data and evaluating the Gamma CDF.

    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  MinusLogGammaScaling.Par
      Parameterization class.
    • Field Summary

      Fields 
      Modifier and Type Field Description
      (package private) double max
      Maximum value seen
      (package private) double mlogmax
      Minimum value (after log step, so maximum again)
    • Field Detail

      • max

        double max
        Maximum value seen
      • mlogmax

        double mlogmax
        Minimum value (after log step, so maximum again)
    • Constructor Detail

      • MinusLogGammaScaling

        public MinusLogGammaScaling()
        Constructor.
    • Method Detail

      • 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
        Overrides:
        prepare in class OutlierGammaScaling
        Parameters:
        or - Outlier result to use