de.lmu.ifi.dbs.elki.algorithm.clustering.subspace
Class CLIQUE<V extends NumberVector<V,?>>

java.lang.Object
  de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<Clustering<SubspaceModel<V>>>
      de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.CLIQUE<V>

Type Parameters:

V - the type of NumberVector handled by this Algorithm

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<SubspaceModel<V>>>, InspectionUtilFrequentlyScanned, Parameterizable

@Title(value="CLIQUE: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications")
@Description(value="Grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.")
@Reference(authors="R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan",
           title="Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications",
           booktitle="Proc. SIGMOD Conference, Seattle, WA, 1998",
           url="http://dx.doi.org/10.1145/276304.276314")
public class CLIQUE<V extends NumberVector<V,?>>
extends AbstractAlgorithm<Clustering<SubspaceModel<V>>>
implements ClusteringAlgorithm<Clustering<SubspaceModel<V>>>

Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.

The implementation consists of two steps:
1. Identification of subspaces that contain clusters
2. Identification of clusters

The third step of the original algorithm (Generation of minimal description for the clusters) is not (yet) implemented.

Reference:
R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications.
In Proc. ACM SIGMOD Int. Conf. on Management of Data, Seattle, WA, 1998.

Nested Class Summary

static class

CLIQUE.Parameterizer<V extends NumberVector<V,?>> Parameterization class.

Field Summary

private static Logging	logger The logger for this class.
private boolean	prune Holds the value of PRUNE_ID.
static OptionID	PRUNE_ID Flag to indicate that only subspaces with large coverage (i.e. the fraction of the database that is covered by the dense units) are selected, the rest will be pruned.
private double	tau Holds the value of TAU_ID.
static OptionID	TAU_ID Parameter to specify the density threshold for the selectivity of a unit, where the selectivity is the fraction of total feature vectors contained in this unit, must be a double greater than 0 and less than 1.
private int	xsi Holds the value of XSI_ID.
static OptionID	XSI_ID Parameter to specify the number of intervals (units) in each dimension, must be an integer greater than 0.

Constructor Summary

CLIQUE(int xsi, double tau, boolean prune)
Constructor.

Method Summary

private double[][]	computeDiffs(List<CLIQUESubspace<V>> denseSubspaces, int[] mi, int[] mp) The specified sorted list of dense subspaces is divided into the selected set I and the pruned set P.
private int[][]	computeMeans(List<CLIQUESubspace<V>> denseSubspaces) The specified sorted list of dense subspaces is divided into the selected set I and the pruned set P.
private List<Pair<Subspace<V>,ModifiableDBIDs>>	determineClusters(List<CLIQUESubspace<V>> denseSubspaces) Determines the clusters in the specified dense subspaces.
private List<CLIQUESubspace<V>>	findDenseSubspaceCandidates(Relation<V> database, List<CLIQUESubspace<V>> denseSubspaces) Determines the k-dimensional dense subspace candidates from the specified (k-1)-dimensional dense subspaces.
private List<CLIQUESubspace<V>>	findDenseSubspaces(Relation<V> database, List<CLIQUESubspace<V>> denseSubspaces) Determines the k-dimensional dense subspaces and performs a pruning if this option is chosen.
private List<CLIQUESubspace<V>>	findOneDimensionalDenseSubspaceCandidates(Relation<V> database) Determines the one-dimensional dense subspace candidates by making a pass over the database.
private List<CLIQUESubspace<V>>	findOneDimensionalDenseSubspaces(Relation<V> database) Determines the one dimensional dense subspaces and performs a pruning if this option is chosen.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
private Collection<CLIQUEUnit<V>>	initOneDimensionalUnits(Relation<V> database) Initializes and returns the one dimensional units.
private List<CLIQUESubspace<V>>	pruneDenseSubspaces(List<CLIQUESubspace<V>> denseSubspaces) Performs a MDL-based pruning of the specified dense subspaces as described in the CLIQUE algorithm.
Clustering<SubspaceModel<V>>	run(Relation<V> relation) Performs the CLIQUE algorithm on the given database.
private void	updateMinMax(V featureVector, double[] minima, double[] maxima) Updates the minima and maxima array according to the specified feature vector.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

makeParameterDistanceFunction, run

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.algorithm.clustering.ClusteringAlgorithm

run

Field Detail

logger

private static final Logging logger

The logger for this class.

XSI_ID

public static final OptionID XSI_ID

Parameter to specify the number of intervals (units) in each dimension, must be an integer greater than 0.

Key: -clique.xsi

TAU_ID

public static final OptionID TAU_ID

Parameter to specify the density threshold for the selectivity of a unit, where the selectivity is the fraction of total feature vectors contained in this unit, must be a double greater than 0 and less than 1.

Key: -clique.tau

PRUNE_ID

public static final OptionID PRUNE_ID

Flag to indicate that only subspaces with large coverage (i.e. the fraction of the database that is covered by the dense units) are selected, the rest will be pruned.

Key: -clique.prune

xsi

private int xsi

Holds the value of XSI_ID.

tau

private double tau

Holds the value of TAU_ID.

prune

private boolean prune

Holds the value of PRUNE_ID.

Constructor Detail

CLIQUE

public CLIQUE(int xsi,
              double tau,
              boolean prune)

Constructor.

Parameters:

xsi - Xsi value

tau - Tau value

prune - Prune flag

Method Detail

run

public Clustering<SubspaceModel<V>> run(Relation<V> relation)

Performs the CLIQUE algorithm on the given database.

Parameters:

relation - Data relation to process

Returns:

Clustering result

determineClusters

private List<Pair<Subspace<V>,ModifiableDBIDs>> determineClusters(List<CLIQUESubspace<V>> denseSubspaces)

Determines the clusters in the specified dense subspaces.

Parameters:

denseSubspaces - the dense subspaces in reverse order by their coverage

Returns:

the clusters in the specified dense subspaces and the corresponding cluster models

findOneDimensionalDenseSubspaces

private List<CLIQUESubspace<V>> findOneDimensionalDenseSubspaces(Relation<V> database)

Determines the one dimensional dense subspaces and performs a pruning if this option is chosen.

Parameters:

database - the database to run the algorithm on

Returns:

the one dimensional dense subspaces reverse ordered by their coverage

findDenseSubspaces

private List<CLIQUESubspace<V>> findDenseSubspaces(Relation<V> database,
                                                   List<CLIQUESubspace<V>> denseSubspaces)

Determines the k-dimensional dense subspaces and performs a pruning if this option is chosen.

Parameters:

database - the database to run the algorithm on

denseSubspaces - the (k-1)-dimensional dense subspaces

Returns:

a list of the k-dimensional dense subspaces sorted in reverse order by their coverage

initOneDimensionalUnits

private Collection<CLIQUEUnit<V>> initOneDimensionalUnits(Relation<V> database)

Initializes and returns the one dimensional units.

Parameters:

database - the database to run the algorithm on

Returns:

the created one dimensional units

updateMinMax

private void updateMinMax(V featureVector,
                          double[] minima,
                          double[] maxima)

Updates the minima and maxima array according to the specified feature vector.

Parameters:

featureVector - the feature vector

minima - the array of minima

maxima - the array of maxima

findOneDimensionalDenseSubspaceCandidates

private List<CLIQUESubspace<V>> findOneDimensionalDenseSubspaceCandidates(Relation<V> database)

Determines the one-dimensional dense subspace candidates by making a pass over the database.

Parameters:

database - the database to run the algorithm on

Returns:

the one-dimensional dense subspace candidates reverse ordered by their coverage

findDenseSubspaceCandidates

private List<CLIQUESubspace<V>> findDenseSubspaceCandidates(Relation<V> database,
                                                            List<CLIQUESubspace<V>> denseSubspaces)

Determines the k-dimensional dense subspace candidates from the specified (k-1)-dimensional dense subspaces.

Parameters:

database - the database to run the algorithm on

denseSubspaces - the (k-1)-dimensional dense subspaces

Returns:

a list of the k-dimensional dense subspace candidates reverse ordered by their coverage

pruneDenseSubspaces

private List<CLIQUESubspace<V>> pruneDenseSubspaces(List<CLIQUESubspace<V>> denseSubspaces)

Performs a MDL-based pruning of the specified dense subspaces as described in the CLIQUE algorithm.

Parameters:

denseSubspaces - the subspaces to be pruned sorted in reverse order by their coverage

Returns:

the subspaces which are not pruned reverse ordered by their coverage

computeMeans

private int[][] computeMeans(List<CLIQUESubspace<V>> denseSubspaces)

The specified sorted list of dense subspaces is divided into the selected set I and the pruned set P. For each set the mean of the cover fractions is computed.

Parameters:

denseSubspaces - the dense subspaces in reverse order by their coverage

Returns:

the mean of the cover fractions, the first value is the mean of the selected set I, the second value is the mean of the pruned set P.

computeDiffs

private double[][] computeDiffs(List<CLIQUESubspace<V>> denseSubspaces,
                                int[] mi,
                                int[] mp)

The specified sorted list of dense subspaces is divided into the selected set I and the pruned set P. For each set the difference from the specified mean values is computed.

Parameters:

denseSubspaces - denseSubspaces the dense subspaces in reverse order by their coverage

mi - the mean of the selected sets I

mp - the mean of the pruned sets P

Returns:

the difference from the specified mean values, the first value is the difference from the mean of the selected set I, the second value is the difference from the mean of the pruned set P.

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<Clustering<SubspaceModel<V extends NumberVector<V,?>>>>

Returns:

Type restriction

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<SubspaceModel<V extends NumberVector<V,?>>>>

Returns:

the static logger