Package elki.outlier.spatial

Class CTLuMedianAlgorithm<N>

  • Type Parameters:
    N - Neighborhood type
    All Implemented Interfaces:
    elki.Algorithm, OutlierAlgorithm
    @Title("Median Algorithm for Spatial Outlier Detection")
@Reference(authors="C.-T. Lu, D. Chen, Y. Kou",
           title="Algorithms for Spatial Outlier Detection",
           booktitle="Proc. 3rd IEEE International Conference on Data Mining",
           url="https://doi.org/10.1109/ICDM.2003.1250986",
           bibkey="DBLP:conf/icdm/LuCK03")
public class CTLuMedianAlgorithm<N>
extends AbstractNeighborhoodOutlier<N>
    Median Algorithm of C.-T. Lu

    Reference:

    C.-T. Lu, D. Chen, Y. Kou
    Algorithms for Spatial Outlier Detection
    Proc. 3rd IEEE International Conference on Data Mining (ICDM)

    Median Algorithm uses Median to represent the average non-spatial attribute value of neighbors.
    The Difference e = non-spatial-Attribute-Value - Median (Neighborhood) is computed.
    The Spatial Objects with the highest standardized e value are Spatial Outliers.

    Since:
    0.4.0
    Author:
    Ahmed Hettab

    • Constructor Detail

      • CTLuMedianAlgorithm

        public CTLuMedianAlgorithm​(NeighborSetPredicate.Factory<N> npredf)
        Constructor.
        Parameters:
        npredf - Neighborhood predicate

    • Method Detail

      • getInputTypeRestriction

        public elki.data.type.TypeInformation[] getInputTypeRestriction()

      • run

        public OutlierResult run​(elki.database.Database database,
                         elki.database.relation.Relation<N> nrel,
                         elki.database.relation.Relation<? extends elki.data.NumberVector> relation)
        Main method.
        Parameters:
        database - Database
        nrel - Neighborhood relation
        relation - Data relation (1d!)
        Returns:
        Outlier detection result