Package elki.outlier

Class GaussianUniformMixture

  • All Implemented Interfaces:
    Algorithm, OutlierAlgorithm

    @Title("Gaussian-Uniform Mixture Model Outlier Detection")
    @Description("Fits a mixture model consisting of a Gaussian and a uniform distribution to the data.")
    @Reference(prefix="Generalization using the likelihood gain as outlier score of",
               authors="E. Eskin",
               title="Anomaly detection over noisy data using learned probability distributions",
               booktitle="Proc. 17th Int. Conf. on Machine Learning (ICML-2000)",
    public class GaussianUniformMixture
    extends java.lang.Object
    implements OutlierAlgorithm
    Outlier detection algorithm using a mixture model approach. The data is modeled as a mixture of two distributions, a Gaussian distribution for ordinary data and a uniform distribution for outliers. At first all Objects are in the set of normal objects and the set of anomalous objects is empty. An iterative procedure then transfers objects from the ordinary set to the anomalous set if the transfer increases the overall likelihood of the data.


    E. Eskin
    Anomaly detection over noisy data using learned probability distributions
    Proc. 17th Int. Conf. on Machine Learning (ICML-2000)

    Lisa Reichert
    • Field Detail

      • MAX_ITER

        private static final int MAX_ITER
        Maximum number of iterations to do.
        See Also:
        Constant Field Values
      • c

        private double c
        Holds the cutoff value.
      • logl

        private double logl
        log(l) precomputed
      • logml

        private double logml
        log(1-l) precomputed
    • Constructor Detail

      • GaussianUniformMixture

        public GaussianUniformMixture​(double l,
                                      double c)
        Constructor with parameters.
        l - l value
        c - c value
    • Method Detail

      • getInputTypeRestriction

        public TypeInformation[] getInputTypeRestriction()
        Description copied from interface: Algorithm
        Get the input type restriction used for negotiating the data query.
        Specified by:
        getInputTypeRestriction in interface Algorithm
        Type restriction
      • loglikelihoodAnomalous

        private double loglikelihoodAnomalous​(DBIDs anomalousObjs)
        Loglikelihood anomalous objects. Uniform distribution.
        anomalousObjs -
        loglikelihood for anomalous objects
      • loglikelihoodNormal

        private double loglikelihoodNormal​(DBIDs objids,
                                           SetDBIDs anomalous,
                                           CovarianceMatrix builder,
                                           Relation<? extends NumberVector> relation)
        Computes the loglikelihood of all normal objects. Gaussian model
        objids - Object IDs for 'normal' objects.
        builder - Covariance matrix builder
        relation - Database
        loglikelihood for normal objects