Class SlopeDependence

  • All Implemented Interfaces:
    Dependence
    Direct Known Subclasses:
    SlopeInversionDependence

    @Reference(authors="Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek",
               title="Interactive Data Mining with 3D-Parallel-Coordinate-Trees",
               booktitle="Proc. 2013 ACM Int. Conf. on Management of Data (SIGMOD 2013)",
               url="https://doi.org/10.1145/2463676.2463696",
               bibkey="DBLP:conf/sigmod/AchtertKSZ13")
    public class SlopeDependence
    extends java.lang.Object
    implements Dependence
    Arrange dimensions based on the entropy of the slope spectrum.

    This version only accepts positive correlations, see also SlopeInversionDependence.

    Reference:

    Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek:
    Interactive Data Mining with 3D-Parallel-Coordinate-Trees.
    Proc. 2013 ACM Int. Conf. on Management of Data (SIGMOD 2013)

    TODO: shouldn't this be normalized by the single-dimension entropies or so?

    Since:
    0.5.5
    Author:
    Erich Schubert, Robert Rödler
    • Field Detail

      • LOG_PRECISION

        protected static final double LOG_PRECISION
        Precision for entropy normalization.
    • Constructor Detail

      • SlopeDependence

        protected SlopeDependence()
        Constructor. Use static instance instead!
    • Method Detail

      • dependence

        public <A,​B> double dependence​(NumberArrayAdapter<?,​A> adapter1,
                                             A data1,
                                             NumberArrayAdapter<?,​B> adapter2,
                                             B data2)
        Description copied from interface: Dependence
        Measure the dependence of two variables.

        This is the more flexible API, which allows using different internal data representations.

        Specified by:
        dependence in interface Dependence
        Type Parameters:
        A - First array type
        B - Second array type
        Parameters:
        adapter1 - First data adapter
        data1 - First data set
        adapter2 - Second data adapter
        data2 - Second data set
        Returns:
        Dependence measure