de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Class DOC<V extends NumberVector<?>>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<Clustering<SubspaceModel<V>>>

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DOC<V>

Type Parameters:

V - the type of NumberVector handled by this Algorithm.

All Implemented Interfaces:

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

@Title(value="DOC: Density-based Optimal projective Clustering")
@Reference(authors="C. M. Procopiuc, M. Jones, P. K. Agarwal, T. M. Murali",
           title="A Monte Carlo algorithm for fast projective clustering",
           booktitle="Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD \'02)",
           url="http://dx.doi.org/10.1145/564691.564739")
public class DOC<V extends NumberVector<?>>
extends AbstractAlgorithm<Clustering<SubspaceModel<V>>>
implements SubspaceClusteringAlgorithm<SubspaceModel<V>>

Provides the DOC algorithm, and it's heuristic variant, FastDOC. DOC is a sampling based subspace clustering algorithm.

Reference:
C. M. Procopiuc, M. Jones, P. K. Agarwal, T. M. Murali
A Monte Carlo algorithm for fast projective clustering.
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '02).

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	DOC.Parameterizer<V extends NumberVector<?>> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private double	alpha Relative density threshold parameter alpha.
private double	beta Balancing parameter for importance of points vs. dimensions
private int	d_zero Holds the value of DOC.Parameterizer.D_ZERO_ID.
private boolean	heuristics Holds the value of DOC.Parameterizer.HEURISTICS_ID.
private static Logging	LOG The logger for this class.
private RandomFactory	rnd Randomizer used internally for sampling points.
private double	w Half width parameter.

Constructor Summary

Constructors

Constructor and Description
DOC(double alpha, double beta, double w, boolean heuristics, int d_zero, RandomFactory random) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private double	computeClusterQuality(int clusterSize, int numRelevantDimensions) Computes the quality of a cluster based on its size and number of relevant attributes, as described via the μ-function from the paper.
private boolean	dimensionIsRelevant(int dimension, Relation<V> relation, DBIDs points) Utility method to test if a given dimension is relevant as determined via a set of reference points (i.e. if the variance along the attribute is lower than the threshold).
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
private Cluster<SubspaceModel<V>>	makeCluster(Relation<V> relation, DBIDs C, BitSet D) Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes.
Clustering<SubspaceModel<V>>	run(Database database, Relation<V> relation) Performs the DOC or FastDOC (as configured) algorithm on the given Database.
private Cluster<SubspaceModel<V>>	runDOC(Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize) Performs a single run of DOC, finding a single cluster.
private Cluster<SubspaceModel<V>>	runFastDOC(Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r) Performs a single run of FastDOC, finding a single cluster.

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

LOG

private static final Logging LOG

The logger for this class.

alpha

private double alpha

Relative density threshold parameter alpha.

beta

private double beta

Balancing parameter for importance of points vs. dimensions

w

private double w

Half width parameter.

heuristics

private boolean heuristics

Holds the value of DOC.Parameterizer.HEURISTICS_ID.

d_zero

private int d_zero

Holds the value of DOC.Parameterizer.D_ZERO_ID.

rnd

private RandomFactory rnd

Randomizer used internally for sampling points.

Constructor Detail

DOC

public DOC(double alpha,
   double beta,
   double w,
   boolean heuristics,
   int d_zero,
   RandomFactory random)

Constructor.

Parameters:

alpha - α relative density threshold.

beta - β balancing parameter for size vs. dimensionality.

w - w half width parameter.

heuristics - whether to use heuristics (FastDOC) or not.

random - Random factory

Method Detail

run

public Clustering<SubspaceModel<V>> run(Database database,
                               Relation<V> relation)

Performs the DOC or FastDOC (as configured) algorithm on the given Database.

This will run exhaustively, i.e. run DOC until no clusters are found anymore / the database size has shrunk below the threshold for minimum cluster size.

Parameters:

database - Database

relation - Data relation

runDOC

private Cluster<SubspaceModel<V>> runDOC(Relation<V> relation,
                               ArrayModifiableDBIDs S,
                               int d,
                               int n,
                               int m,
                               int r,
                               int minClusterSize)

Performs a single run of DOC, finding a single cluster.

Parameters:

relation - used to get actual values for DBIDs.

S - The set of points we're working on.

d - Dimensionality of the data set we're currently working on.

r - Size of random samples.

m - Number of inner iterations (per seed point).

n - Number of outer iterations (seed points).

minClusterSize - Minimum size a cluster must have to be accepted.

Returns:

a cluster, if one is found, else null.

runFastDOC

private Cluster<SubspaceModel<V>> runFastDOC(Relation<V> relation,
                                   ArrayModifiableDBIDs S,
                                   int d,
                                   int n,
                                   int m,
                                   int r)

Performs a single run of FastDOC, finding a single cluster.

Parameters:

relation - used to get actual values for DBIDs.

S - The set of points we're working on.

d - Dimensionality of the data set we're currently working on.

r - Size of random samples.

m - Number of inner iterations (per seed point).

n - Number of outer iterations (seed points).

Returns:

a cluster, if one is found, else null.

dimensionIsRelevant

private boolean dimensionIsRelevant(int dimension,
                          Relation<V> relation,
                          DBIDs points)

Utility method to test if a given dimension is relevant as determined via a set of reference points (i.e. if the variance along the attribute is lower than the threshold).

Parameters:

dimension - the dimension to test.

relation - used to get actual values for DBIDs.

points - the points to test.

Returns:

true if the dimension is relevant.

makeCluster

private Cluster<SubspaceModel<V>> makeCluster(Relation<V> relation,
                                    DBIDs C,
                                    BitSet D)

Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes.

Parameters:

relation - to compute a centroid.

C - the cluster points.

D - the relevant dimensions.

Returns:

an object representing the subspace cluster.

computeClusterQuality

private double computeClusterQuality(int clusterSize,
                           int numRelevantDimensions)

Computes the quality of a cluster based on its size and number of relevant attributes, as described via the μ-function from the paper.

Parameters:

clusterSize - the size of the cluster.

numRelevantDimensions - the number of dimensions relevant to the cluster.

Returns:

a quality measure (only use this to compare the quality to that other clusters).

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<?>>>>

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<?>>>>

Returns:

the static logger