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

Class SUBCLU<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.SUBCLU<V>

Type Parameters:

V - the type of FeatureVector handled by this Algorithm

All Implemented Interfaces:

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

@Title(value="SUBCLU: Density connected Subspace Clustering")
@Description(value="Algorithm to detect arbitrarily shaped and positioned clusters in subspaces. SUBCLU delivers for each subspace the same clusters DBSCAN would have found, when applied to this subspace seperately.")
@Reference(authors="K. Kailing, H.-P. Kriegel, P. Kr\u00f6ger",
           title="Density connected Subspace Clustering for High Dimensional Data. ",
           booktitle="Proc. SIAM Int. Conf. on Data Mining (SDM\'04), Lake Buena Vista, FL, 2004")
public class SUBCLU<V extends NumberVector<V,?>>
extends AbstractAlgorithm<Clustering<SubspaceModel<V>>>
implements ClusteringAlgorithm<Clustering<SubspaceModel<V>>>

Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces. SUBCLU delivers for each subspace the same clusters DBSCAN would have found, when applied to this subspace separately.

Reference:
K. Kailing, H.-P. Kriegel, P. Kroeger: Density connected Subspace Clustering for High Dimensional Data.
In Proc. SIAM Int. Conf. on Data Mining (SDM'04), Lake Buena Vista, FL, 2004.

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
static OptionID	DISTANCE_FUNCTION_ID The distance function to determine the distance between database objects.
private AbstractDimensionsSelectingDoubleDistanceFunction<V>	distanceFunction Holds the instance of the distance function specified by DISTANCE_FUNCTION_ID.
private DoubleDistance	epsilon Holds the value of EPSILON_ID.
static OptionID	EPSILON_ID Parameter to specify the maximum radius of the neighborhood to be considered, must be suitable to AbstractDimensionsSelectingDoubleDistanceFunction.
private static Logging	logger The logger for this class.
private int	minpts Holds the value of MINPTS_ID.
static OptionID	MINPTS_ID Parameter to specify the threshold for minimum number of points in the epsilon-neighborhood of a point, must be an integer greater than 0.
private Clustering<SubspaceModel<V>>	result Holds the result;

Constructor Summary

Constructors

Constructor and Description
SUBCLU(AbstractDimensionsSelectingDoubleDistanceFunction<V> distanceFunction, DoubleDistance epsilon, int minpts) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private Subspace<V>	bestSubspace(List<Subspace<V>> subspaces, Subspace<V> candidate, TreeMap<Subspace<V>,List<Cluster<Model>>> clusterMap) Determines the d-dimensional subspace of the (d+1) -dimensional candidate with minimal number of objects in the cluster.
private List<Subspace<V>>	generateSubspaceCandidates(List<Subspace<V>> subspaces) Generates d+1-dimensional subspace candidates from the specified d-dimensional subspaces.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
Clustering<SubspaceModel<V>>	getResult() Returns the result of the algorithm.
private List<Subspace<V>>	lowerSubspaces(Subspace<V> subspace) Returns the list of all (d-1)-dimensional subspaces of the specified d-dimensional subspace.
Clustering<SubspaceModel<V>>	run(Relation<V> relation) Performs the SUBCLU algorithm on the given database.
private List<Cluster<Model>>	runDBSCAN(Relation<V> relation, DBIDs ids, Subspace<V> subspace) Runs the DBSCAN algorithm on the specified partition of the database in the given subspace.

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.

DISTANCE_FUNCTION_ID

public static final OptionID DISTANCE_FUNCTION_ID

The distance function to determine the distance between database objects.

Default value: DimensionsSelectingEuclideanDistanceFunction

Key: -subclu.distancefunction

EPSILON_ID

public static final OptionID EPSILON_ID

Parameter to specify the maximum radius of the neighborhood to be considered, must be suitable to AbstractDimensionsSelectingDoubleDistanceFunction.

Key: -subclu.epsilon

MINPTS_ID

public static final OptionID MINPTS_ID

Parameter to specify the threshold for minimum number of points in the epsilon-neighborhood of a point, must be an integer greater than 0.

Key: -subclu.minpts

distanceFunction

private AbstractDimensionsSelectingDoubleDistanceFunction<V extends NumberVector<V,?>> distanceFunction

Holds the instance of the distance function specified by DISTANCE_FUNCTION_ID.

epsilon

private DoubleDistance epsilon

Holds the value of EPSILON_ID.

minpts

private int minpts

Holds the value of MINPTS_ID.

result

private Clustering<SubspaceModel<V extends NumberVector<V,?>>> result

Holds the result;

Constructor Detail

SUBCLU

public SUBCLU(AbstractDimensionsSelectingDoubleDistanceFunction<V> distanceFunction,
      DoubleDistance epsilon,
      int minpts)

Constructor.

Parameters:

distanceFunction - Distance function

epsilon - Epsilon value

minpts - Minpts value

Method Detail

run

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

Performs the SUBCLU algorithm on the given database.

Parameters:

relation - Relation to process

Returns:

Clustering result

Throws:

IllegalStateException

getResult

public Clustering<SubspaceModel<V>> getResult()

Returns the result of the algorithm.

Returns:

the result of the algorithm

runDBSCAN

private List<Cluster<Model>> runDBSCAN(Relation<V> relation,
                             DBIDs ids,
                             Subspace<V> subspace)

Runs the DBSCAN algorithm on the specified partition of the database in the given subspace. If parameter ids is null DBSCAN will be applied to the whole database.

Parameters:

relation - the database holding the objects to run DBSCAN on

ids - the IDs of the database defining the partition to run DBSCAN on - if this parameter is null DBSCAN will be applied to the whole database

subspace - the subspace to run DBSCAN on

Returns:

the clustering result of the DBSCAN run

generateSubspaceCandidates

private List<Subspace<V>> generateSubspaceCandidates(List<Subspace<V>> subspaces)

Generates d+1-dimensional subspace candidates from the specified d-dimensional subspaces.

Parameters:

subspaces - the d-dimensional subspaces

Returns:

the d+1-dimensional subspace candidates

lowerSubspaces

private List<Subspace<V>> lowerSubspaces(Subspace<V> subspace)

Returns the list of all (d-1)-dimensional subspaces of the specified d-dimensional subspace.

Parameters:

subspace - the d-dimensional subspace

Returns:

a list of all (d-1)-dimensional subspaces

bestSubspace

private Subspace<V> bestSubspace(List<Subspace<V>> subspaces,
                       Subspace<V> candidate,
                       TreeMap<Subspace<V>,List<Cluster<Model>>> clusterMap)

Determines the d-dimensional subspace of the (d+1) -dimensional candidate with minimal number of objects in the cluster.

Parameters:

subspaces - the list of d-dimensional subspaces containing clusters

candidate - the (d+1)-dimensional candidate subspace

clusterMap - the mapping of subspaces to clusters

Returns:

the d-dimensional subspace of the (d+1) -dimensional candidate with minimal number of objects in the cluster

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