Class CTLuMeanMultipleAttributes<N,​O extends NumberVector>

  • Type Parameters:
    N - Spatial Vector
    O - Attribute Vector
    All Implemented Interfaces:
    Algorithm, OutlierAlgorithm

    @Reference(authors="C.-T. Lu, D. Chen, Y. Kou",
               title="Detecting Spatial Outliers with Multiple Attributes",
               booktitle="Proc. 15th IEEE Int. Conf. Tools with Artificial Intelligence (TAI 2003)",
               url="https://doi.org/10.1109/TAI.2003.1250179",
               bibkey="DBLP:conf/ictai/LuCK03")
    public class CTLuMeanMultipleAttributes<N,​O extends NumberVector>
    extends AbstractNeighborhoodOutlier<N>
    Mean Approach is used to discover spatial outliers with multiple attributes.

    Reference:

    C.-T. Lu, D. Chen, Y. Kou
    Detecting Spatial Outliers with Multiple Attributes
    Proc. 15th IEEE Int. Conf. Tools with Artificial Intelligence (TAI 2003)

    Implementation note: attribute standardization is not used; this is equivalent to using the AttributeWiseVarianceNormalization filter.

    Since:
    0.4.0
    Author:
    Ahmed Hettab
    • Field Detail

      • LOG

        private static final Logging LOG
        Logger
    • Constructor Detail

      • CTLuMeanMultipleAttributes

        public CTLuMeanMultipleAttributes​(NeighborSetPredicate.Factory<N> npredf)
        Constructor
        Parameters:
        npredf - Neighborhood predicate
    • Method Detail

      • getInputTypeRestriction

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

        public OutlierResult run​(Database database,
                                 Relation<N> spatial,
                                 Relation<O> attributes)
        Run the algorithm
        Parameters:
        database - Database
        spatial - Spatial relation
        attributes - Numerical attributes
        Returns:
        Outlier detection result