Class SURFINGDependence
- java.lang.Object
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- elki.math.statistics.dependence.SURFINGDependence
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- All Implemented Interfaces:
Dependence
@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="Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, Peer Kr\u00f6ger",title="Subspace Selection for Clustering High-Dimensional Data",booktitle="Proc. IEEE International Conference on Data Mining (ICDM 2004)",url="https://doi.org/10.1109/ICDM.2004.10112",bibkey="DBLP:conf/icdm/BaumgartnerPKKK04") @Priority(-100) public class SURFINGDependence extends java.lang.Object implements Dependence
Compute the similarity of dimensions using the SURFING score. The parameter k for the k nearest neighbors is currently hard-coded to 10% of the set size.Note that the complexity is roughly O(n n k), so this is a rather slow method, and with k at 10% of n, is actually cubic: O(0.1 * n²).
This version cannot use index support, as the API operates without database attachment. However, it should be possible to implement some trivial sorted-list indexes to get a reasonable speedup!
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:
Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, Peer Kröger
Subspace Selection for Clustering High-Dimensional Data
Proc. IEEE International Conference on Data Mining (ICDM 2004)TODO: make the subspace distance function and k parameterizable.
TODO: results are not convincing, maybe try inserting points.
- Since:
- 0.5.5
- Author:
- Robert Rödler, Erich Schubert
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
SURFINGDependence.Par
Parameterization class.-
Nested classes/interfaces inherited from interface elki.math.statistics.dependence.Dependence
Dependence.Utils
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Field Summary
Fields Modifier and Type Field Description static SURFINGDependence
STATIC
Static instance.
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Constructor Summary
Constructors Modifier Constructor Description protected
SURFINGDependence()
Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description <A,B>
doubledependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
Measure the dependence of two variables.-
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface elki.math.statistics.dependence.Dependence
dependence, dependence, dependence
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Field Detail
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STATIC
public static final SURFINGDependence STATIC
Static instance.
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Method Detail
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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 interfaceDependence
- Type Parameters:
A
- First array typeB
- Second array type- Parameters:
adapter1
- First data adapterdata1
- First data setadapter2
- Second data adapterdata2
- Second data set- Returns:
- Dependence measure
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