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

Class FastDOC<V extends NumberVector>

java.lang.Object

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

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

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

Type Parameters:

V - the type of NumberVector handled by this Algorithm.

All Implemented Interfaces:

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

@Title(value="FastDOC: 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="https://doi.org/10.1145/564691.564739",
           bibkey="DBLP:conf/sigmod/ProcopiucJAM02")
public class FastDOC<V extends NumberVector>
extends DOC<V>

The heuristic variant of the DOC algorithm, FastDOC

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).

Since:

0.7.5

Author:

Florian Nuecke

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
private int	d_zero Holds the value of FastDOC.Parameterizer.D_ZERO_ID.
private static Logging	LOG The logger for this class.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DOC

alpha, beta, rnd, w

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

ALGORITHM_ID

Constructor Summary

Constructors

Constructor and Description
FastDOC(double alpha, double beta, double w, int d_zero, RandomFactory random) Constructor.

Method Summary

Modifier and Type	Method and Description
protected Logging	getLogger() Get the (STATIC) logger for this class.
protected Cluster<SubspaceModel>	runDOC(Database database, Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize) Performs a single run of FastDOC, finding a single cluster.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DOC

computeClusterQuality, dimensionIsRelevant, findNeighbors, getInputTypeRestriction, makeCluster, run

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

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.

d_zero

private int d_zero

Holds the value of FastDOC.Parameterizer.D_ZERO_ID.

Constructor Detail

FastDOC

public FastDOC(double alpha,
               double beta,
               double w,
               int d_zero,
               RandomFactory random)

Constructor.

Parameters:

alpha - α relative density threshold.

beta - β balancing parameter for size vs. dimensionality.

w - half width parameter.

random - Random factory

Method Detail

runDOC

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

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

Overrides:

runDOC in class DOC<V extends NumberVector>

Parameters:

database - Database context

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.

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Overrides:

getLogger in class DOC<V extends NumberVector>

Returns:

the static logger