Package elki.clustering.subspace

Class FastDOC

java.lang.Object

elki.clustering.subspace.DOC

elki.clustering.subspace.FastDOC

All Implemented Interfaces:

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

@Title("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
extends DOC

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	Description
static class	FastDOC.Par	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm

elki.Algorithm.Utils

Field Summary

Fields

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

Fields inherited from class elki.clustering.subspace.DOC

alpha, beta, rnd, w

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method	Description
protected Cluster<SubspaceModel>	runDOC(elki.database.relation.Relation<? extends elki.data.NumberVector> relation, elki.database.ids.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 elki.clustering.subspace.DOC

computeClusterQuality, dimensionIsRelevant, findNeighbors, getInputTypeRestriction, makeCluster, run

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm

autorun

Field Detail

LOG

private static final elki.logging.Logging LOG

The logger for this class.

d_zero

private int d_zero

Holds the value of FastDOC.Par.D_ZERO_ID.

Constructor Detail

FastDOC

public FastDOC(double alpha,
               double beta,
               double w,
               int d_zero,
               elki.utilities.random.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(elki.database.relation.Relation<? extends elki.data.NumberVector> relation,
                                        elki.database.ids.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

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.