de.lmu.ifi.dbs.elki.math.dimensionsimilarity

Class SURFINGDimensionSimilarity

java.lang.Object

de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SURFINGDimensionSimilarity

All Implemented Interfaces:

DimensionSimilarity<NumberVector>

@Reference(authors="Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek",
           title="Interactive Data Mining with 3D-Parallel-Coordinate-Trees",
           booktitle="Proc. of the 2013 ACM International Conference on Management of Data (SIGMOD)",
           url="http://dx.doi.org/10.1145/2463676.2463696")
public class SURFINGDimensionSimilarity
extends Object
implements DimensionSimilarity<NumberVector>

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) * O(d^2), so this is a rather slow method without index support, and with k at 10% of n, is actually cubic. So try to use an appropriate index! Reference:

Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek:
Interactive Data Mining with 3D-Parallel-Coordinate-Trees.
Proceedings of the 2013 ACM International Conference on Management of Data (SIGMOD), New York City, NY, 2013.

Based on:

Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, and Peer Kröger
Subspace Selection for Clustering High-Dimensional Data
In IEEE International Conference on Data Mining, 2004.

TODO: make the subspace distance function and k parameterizable.

Author:

Robert Rödler, Erich Schubert

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
static SURFINGDimensionSimilarity	STATIC Static instance.

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	SURFINGDimensionSimilarity() Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	computeDimensionSimilarites(Relation<? extends NumberVector> relation, DBIDs subset, DimensionSimilarityMatrix matrix) Compute the dimension similarity matrix

Methods inherited from class java.lang.Object

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

Field Detail

STATIC

public static final SURFINGDimensionSimilarity STATIC

Static instance.

Constructor Detail

SURFINGDimensionSimilarity

protected SURFINGDimensionSimilarity()

Constructor. Use static instance instead!

Method Detail

computeDimensionSimilarites

@Reference(authors="Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, and Peer Kr\u00f6ger",
           title="Subspace Selection for Clustering High-Dimensional Data",
           booktitle="IEEE International Conference on Data Mining, 2004",
           url="http://dx.doi.org/10.1109/ICDM.2004.10112")
public void computeDimensionSimilarites(Relation<? extends NumberVector> relation,
                                                                 DBIDs subset,
                                                                 DimensionSimilarityMatrix matrix)

Description copied from interface: DimensionSimilarity

Compute the dimension similarity matrix

Specified by:

computeDimensionSimilarites in interface DimensionSimilarity<NumberVector>

Parameters:

relation - Relation

subset - DBID subset (for sampling / selection)

matrix - Matrix to fill