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

Class SUBCLU<V extends NumberVector>

java.lang.Object

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

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

Type Parameters:

V - the type of NumberVector handled by this algorithm

All Implemented Interfaces:

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

@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="Karin Kailing, Hans-Peter Kriegel, Peer Kr\u00f6ger",
           title="Density Connected Subspace Clustering for High Dimensional Data",
           booktitle="Proc. SIAM Int. Conf. on Data Mining (SDM\'04)",
           url="https://doi.org/10.1137/1.9781611972740.23",
           bibkey="DBLP:conf/sdm/KroegerKK04")
public class SUBCLU<V extends NumberVector>
extends AbstractAlgorithm<Clustering<SubspaceModel>>
implements SubspaceClusteringAlgorithm<SubspaceModel>

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.

Note: this implementation does not yet implemented the suggested indexing based on inverted files, and that we also only query subsets of them, this makes the implementation as a reusable index challenging.

The original paper is not very clear on which clusters to return, as any subspace cluster must be part of a lower-dimensional projected cluster, so these results would be highly redundant. In this implementation, we only include points in clusters that are not already part of sub-clusters (note that this does not remove overlap of independent subspaces).

Reference:

Karin Kailing, Hans-Peter Kriegel, Peer Kröger
Density Connected Subspace Clustering for High Dimensional Data
Proc. SIAM Int. Conf. on Data Mining (SDM'04)

Since:

0.3

Author:

Elke Achtert

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
protected DimensionSelectingSubspaceDistanceFunction<V>	distanceFunction The distance function to determine the distance between objects.
protected double	epsilon Maximum radius of the neighborhood to be considered.
private static Logging	LOG The logger for this class.
protected int	mindim Minimum dimensionality.
protected int	minpts Minimum number of points.

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

ALGORITHM_ID

Constructor Summary

Constructors

Constructor and Description
SUBCLU(DimensionSelectingSubspaceDistanceFunction<V> distanceFunction, double epsilon, int minpts, int mindim) Constructor.

Method Summary

Modifier and Type	Method and Description
private Subspace	bestSubspace(java.util.List<Subspace> subspaces, Subspace candidate, java.util.TreeMap<Subspace,java.util.List<Cluster<Model>>> clusterMap) Determines the d-dimensional subspace of the (d+1) -dimensional candidate with minimal number of objects in the cluster.
private boolean	checkLower(Subspace candidate, java.util.List<Subspace> subspaces) Perform Apriori-style pruning.
private java.util.List<Subspace>	generateSubspaceCandidates(java.util.List<Subspace> 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>	run(Relation<V> relation) Performs the SUBCLU algorithm on the given database.
private java.util.List<Cluster<Model>>	runDBSCAN(Relation<V> relation, DBIDs ids, Subspace 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

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.

distanceFunction

protected DimensionSelectingSubspaceDistanceFunction<V extends NumberVector> distanceFunction

The distance function to determine the distance between objects.

epsilon

protected double epsilon

Maximum radius of the neighborhood to be considered.

minpts

protected int minpts

Minimum number of points.

mindim

protected int mindim

Minimum dimensionality.

Constructor Detail

SUBCLU

public SUBCLU(DimensionSelectingSubspaceDistanceFunction<V> distanceFunction,
              double epsilon,
              int minpts,
              int mindim)

Constructor.

Parameters:

distanceFunction - Distance function

epsilon - Epsilon value

minpts - Minpts value

mindim - Minimum dimensionality

Method Detail

run

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

Performs the SUBCLU algorithm on the given database.

Parameters:

relation - Relation to process

Returns:

Clustering result

runDBSCAN

private java.util.List<Cluster<Model>> runDBSCAN(Relation<V> relation,
                                                 DBIDs ids,
                                                 Subspace 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 java.util.List<Subspace> generateSubspaceCandidates(java.util.List<Subspace> 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

checkLower

private boolean checkLower(Subspace candidate,
                           java.util.List<Subspace> subspaces)

Perform Apriori-style pruning.

Parameters:

candidate - Current candidate

subspaces - Subspaces

Returns:

true if all lower-dimensional subspaces exist

bestSubspace

private Subspace bestSubspace(java.util.List<Subspace> subspaces,
                              Subspace candidate,
                              java.util.TreeMap<Subspace,java.util.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>>

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>>

Returns:

the static logger