Package elki.outlier.distance

Class DBOutlierDetection<O>

  • Type Parameters:
    O - the type of objects handled by this algorithm
    All Implemented Interfaces:
    Algorithm, OutlierAlgorithm
    @Title("DBOD: Distance Based Outlier Detection")
@Description("If the D-neighborhood of an object contains only very few objects (less than (1-p) percent of the data) this object is flagged as an outlier")
@Reference(authors="E. M. Knorr, R. T. Ng",
           title="Algorithms for Mining Distance-Based Outliers in Large Datasets",
           booktitle="Proc. Int. Conf. on Very Large Databases (VLDB\'98)",
           url="http://www.vldb.org/conf/1998/p392.pdf",
           bibkey="DBLP:conf/vldb/KnorrN98")
public class DBOutlierDetection<O>
extends AbstractDBOutlier<O>
    Simple distanced based outlier detection algorithm. User has to specify two parameters An object is flagged as an outlier if at least a fraction p of all data objects has a distance above d from c.

    Reference:

    E.M. Knorr, R. T. Ng:
    Algorithms for Mining Distance-Based Outliers in Large Datasets,
    In: Proc. Int. Conf. on Very Large Databases (VLDB'98)

    This paper presents several Distance Based Outlier Detection algorithms. Implemented here is a simple index based algorithm as presented in section 3.1.

    Since:
    0.3
    Author:
    Lisa Reichert

    • Field Detail

      • LOG

        private static final Logging LOG
        The logger for this class.

      • p

        private double p
        Density threshold percentage p.

    • Constructor Detail

      • DBOutlierDetection

        public DBOutlierDetection​(Distance<? super O> distance,
                          double d,
                          double p)
        Constructor with actual parameters.
        Parameters:
        distance - distance function parameter
        d - distance query radius
        p - percentage parameter