de.lmu.ifi.dbs.elki.math.statistics.dependence

Class HiCSDependenceMeasure

java.lang.Object

de.lmu.ifi.dbs.elki.math.statistics.dependence.AbstractDependenceMeasure

de.lmu.ifi.dbs.elki.math.statistics.dependence.HiCSDependenceMeasure

All Implemented Interfaces:

DependenceMeasure

@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") @Reference(authors="F. Keller, E. M\u00fcller, K. B\u00f6hm",title="HiCS: High Contrast Subspaces for Density-Based Outlier Ranking",booktitle="Proc. IEEE 28th Int. Conf. on Data Engineering (ICDE 2012)",url="https://doi.org/10.1109/ICDE.2012.88",bibkey="DBLP:conf/icde/KellerMB12")
public class HiCSDependenceMeasure
extends AbstractDependenceMeasure

Use the statistical tests as used by HiCS to measure dependence of variables.

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)

Based on:

F. Keller, E. Müller, K. Böhm
HiCS: High Contrast Subspaces for Density-Based Outlier Ranking
In ICDE, pages 1037–1048, 2012.

Since:

0.5.5

Author:

Erich Schubert, Robert Rödler

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	HiCSDependenceMeasure.Parameterizer Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private double	alphasqrt Alpha threshold
private int	m Monte-Carlo iterations
private RandomFactory	rnd Random generator
private GoodnessOfFitTest	statTest Statistical test to use

Constructor Summary

Constructors

Constructor and Description
HiCSDependenceMeasure(GoodnessOfFitTest statTest, int m, double alpha, RandomFactory rnd) Constructor.

Method Summary

Modifier and Type	Method and Description
<A,B> double	dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2) Measure the dependence of two variables.

Methods inherited from class de.lmu.ifi.dbs.elki.math.statistics.dependence.AbstractDependenceMeasure

clamp, computeNormalizedRanks, discretize, index, ranks, ranks, size, size, sortedIndex

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.math.statistics.dependence.DependenceMeasure

dependence, dependence, dependence

Field Detail

m

private int m

Monte-Carlo iterations

alphasqrt

private double alphasqrt

Alpha threshold

statTest

private GoodnessOfFitTest statTest

Statistical test to use

rnd

private RandomFactory rnd

Random generator

Constructor Detail

HiCSDependenceMeasure

public HiCSDependenceMeasure(GoodnessOfFitTest statTest,
                             int m,
                             double alpha,
                             RandomFactory rnd)

Constructor.

Parameters:

statTest - Test function

m - Number of monte-carlo iterations

alpha - Alpha threshold

rnd - Random source

Method Detail

dependence

public <A,B> double dependence(NumberArrayAdapter<?,A> adapter1,
                               A data1,
                               NumberArrayAdapter<?,B> adapter2,
                               B data2)

Description copied from interface: DependenceMeasure

Measure the dependence of two variables. This is the more flexible API, which allows using different internal data representations.

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