de.lmu.ifi.dbs.elki.algorithm.clustering
Class AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<V,?>>

java.lang.Object
  de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>
      de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedDBSCAN<R,V>

Type Parameters:

V - the type of NumberVector handled by this Algorithm

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<R>, InspectionUtilFrequentlyScanned, Parameterizable

Direct Known Subclasses:

FourC, PreDeCon

public abstract class AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<V,?>>
extends AbstractAlgorithm<R>
implements ClusteringAlgorithm<R>

Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.

Nested Class Summary

static class

AbstractProjectedDBSCAN.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>> Parameterization class.

Field Summary

private LocallyWeightedDistanceFunction<V>	distanceFunction Holds the instance of the distance function specified by INNER_DISTANCE_FUNCTION_ID.
protected 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 LocallyWeightedDistanceFunction.
static OptionID	INNER_DISTANCE_FUNCTION_ID Parameter distance function
private int	lambda Holds the value of LAMBDA_ID.
static OptionID	LAMBDA_ID Parameter to specify the intrinsic dimensionality of the clusters to find, must be an integer greater than 0.
protected 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 ModifiableDBIDs	noise Holds a set of noise.
static OptionID	OUTER_DISTANCE_FUNCTION_ID Parameter to specify the distance function to determine the distance between database objects, must extend LocallyWeightedDistanceFunction .
private ModifiableDBIDs	processedIDs Holds a set of processed ids.
private List<ModifiableDBIDs>	resultList Holds a list of clusters found.

Constructor Summary

AbstractProjectedDBSCAN(DoubleDistance epsilon, int minpts, LocallyWeightedDistanceFunction<V> distanceFunction, int lambda)
Constructor.

Method Summary

protected void	expandCluster(LocallyWeightedDistanceFunction.Instance<V> distFunc, RangeQuery<V,DoubleDistance> rangeQuery, DBID startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog) ExpandCluster function of DBSCAN.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
abstract String	getLongResultName() Return the long result name.
abstract String	getShortResultName() Return the short result name.
Clustering<Model>	run(Database database, Relation<V> relation)

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

getLogger, 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

OUTER_DISTANCE_FUNCTION_ID

public static final OptionID OUTER_DISTANCE_FUNCTION_ID

Parameter to specify the distance function to determine the distance between database objects, must extend LocallyWeightedDistanceFunction .

Key: -projdbscan.distancefunction

Default value: LocallyWeightedDistanceFunction

INNER_DISTANCE_FUNCTION_ID

public static final OptionID INNER_DISTANCE_FUNCTION_ID

Parameter distance function

EPSILON_ID

public static final OptionID EPSILON_ID

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

Key: -projdbscan.epsilon

LAMBDA_ID

public static final OptionID LAMBDA_ID

Parameter to specify the intrinsic dimensionality of the clusters to find, must be an integer greater than 0.

Key: -projdbscan.lambda

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: -projdbscan.minpts

distanceFunction

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

Holds the instance of the distance function specified by INNER_DISTANCE_FUNCTION_ID.

epsilon

protected DoubleDistance epsilon

Holds the value of EPSILON_ID.

lambda

private int lambda

Holds the value of LAMBDA_ID.

minpts

protected int minpts

Holds the value of MINPTS_ID.

resultList

private List<ModifiableDBIDs> resultList

Holds a list of clusters found.

noise

private ModifiableDBIDs noise

Holds a set of noise.

processedIDs

private ModifiableDBIDs processedIDs

Holds a set of processed ids.

Constructor Detail

AbstractProjectedDBSCAN

public AbstractProjectedDBSCAN(DoubleDistance epsilon,
                               int minpts,
                               LocallyWeightedDistanceFunction<V> distanceFunction,
                               int lambda)

Constructor.

Parameters:

epsilon - Epsilon

minpts - MinPts parameter

distanceFunction - Outer distance function

lambda - Lambda value

Method Detail

run

public Clustering<Model> run(Database database,
                             Relation<V> relation)
                      throws IllegalStateException

Throws:

IllegalStateException

getLongResultName

public abstract String getLongResultName()

Return the long result name.

Returns:

Long name for result

getShortResultName

public abstract String getShortResultName()

Return the short result name.

Returns:

Short name for result

expandCluster

protected void expandCluster(LocallyWeightedDistanceFunction.Instance<V> distFunc,
                             RangeQuery<V,DoubleDistance> rangeQuery,
                             DBID startObjectID,
                             FiniteProgress objprog,
                             IndefiniteProgress clusprog)

ExpandCluster function of DBSCAN.

Parameters:

distFunc - Distance query to use

rangeQuery - Range query

startObjectID - the object id of the database object to start the expansion with

objprog - the progress object for logging the current status

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<R extends Clustering<Model>>

Returns:

Type restriction