de.lmu.ifi.dbs.elki.algorithm.clustering
Class ProjectedDBSCAN<V extends NumberVector<V,?>>

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<V,Clustering<Model>>
          de.lmu.ifi.dbs.elki.algorithm.clustering.ProjectedDBSCAN<V>

Type Parameters:

V - the type of NumberVector handled by this Algorithm

All Implemented Interfaces:

Algorithm<V,Clustering<Model>>, ClusteringAlgorithm<Clustering<Model>,V>, Parameterizable

Direct Known Subclasses:

FourC, PreDeCon

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

Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.

Author:

Arthur Zimek

Field Summary

private AbstractLocallyWeightedDistanceFunction<V,?>	distanceFunction Holds the instance of the distance function specified by INNER_DISTANCE_FUNCTION_PARAM.
protected DoubleDistance	epsilon Holds the value of EPSILON_PARAM.
static OptionID	EPSILON_ID OptionID for EPSILON_PARAM
private DistanceParameter<DoubleDistance>	EPSILON_PARAM Parameter to specify the maximum radius of the neighborhood to be considered, must be suitable to AbstractLocallyWeightedDistanceFunction.
static OptionID	INNER_DISTANCE_FUNCTION_ID OptionID for INNER_DISTANCE_FUNCTION_PARAM
private ObjectParameter<DistanceFunction<V,DoubleDistance>>	INNER_DISTANCE_FUNCTION_PARAM Parameter distance function
private DistanceFunction<V,DoubleDistance>	innerDistanceFunction The inner distance function.
private int	lambda Holds the value of LAMBDA_PARAM.
static OptionID	LAMBDA_ID OptionID for LAMBDA_PARAM
private IntParameter	LAMBDA_PARAM 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_PARAM.
static OptionID	MINPTS_ID OptionID for MINPTS_PARAM
private IntParameter	MINPTS_PARAM 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 Set<Integer>	noise Holds a set of noise.
static OptionID	OUTER_DISTANCE_FUNCTION_ID OptionID for OUTER_DISTANCE_FUNCTION_PARAM
protected ObjectParameter<AbstractLocallyWeightedDistanceFunction<V,?>>	OUTER_DISTANCE_FUNCTION_PARAM Parameter to specify the distance function to determine the distance between database objects, must extend AbstractLocallyWeightedDistanceFunction .
private Set<Integer>	processedIDs Holds a set of processed ids.
private List<List<Integer>>	resultList Holds a list of clusters found.

Fields inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debug, logger

Constructor Summary

ProjectedDBSCAN(Parameterization config)
Constructor, adhering to Parameterizable

Method Summary

protected void	expandCluster(Database<V> database, Integer startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog) ExpandCluster function of DBSCAN.
abstract Class<?>	preprocessorClass() Returns the class actually used as VarianceAnalysisPreprocessor.
protected Clustering<Model>	runInTime(Database<V> database) The run method encapsulated in measure of runtime.

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

isTime, isVerbose, run, setTime, setVerbose

Methods inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debugFine, debugFiner, debugFinest, exception, progress, verbose, warning

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

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.Algorithm

setTime, setVerbose

Field Detail

OUTER_DISTANCE_FUNCTION_ID

public static final OptionID OUTER_DISTANCE_FUNCTION_ID

OptionID for OUTER_DISTANCE_FUNCTION_PARAM

OUTER_DISTANCE_FUNCTION_PARAM

protected final ObjectParameter<AbstractLocallyWeightedDistanceFunction<V extends NumberVector<V,?>,?>> OUTER_DISTANCE_FUNCTION_PARAM

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

Key: -projdbscan.distancefunction

Default value: LocallyWeightedDistanceFunction

INNER_DISTANCE_FUNCTION_ID

public static final OptionID INNER_DISTANCE_FUNCTION_ID

OptionID for INNER_DISTANCE_FUNCTION_PARAM

INNER_DISTANCE_FUNCTION_PARAM

private final ObjectParameter<DistanceFunction<V extends NumberVector<V,?>,DoubleDistance>> INNER_DISTANCE_FUNCTION_PARAM

Parameter distance function

distanceFunction

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

Holds the instance of the distance function specified by INNER_DISTANCE_FUNCTION_PARAM.

EPSILON_ID

public static final OptionID EPSILON_ID

OptionID for EPSILON_PARAM

EPSILON_PARAM

private final DistanceParameter<DoubleDistance> EPSILON_PARAM

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

Key: -projdbscan.epsilon

epsilon

protected DoubleDistance epsilon

Holds the value of EPSILON_PARAM.

LAMBDA_ID

public static final OptionID LAMBDA_ID

OptionID for LAMBDA_PARAM

LAMBDA_PARAM

private final IntParameter LAMBDA_PARAM

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

Key: -projdbscan.lambda

lambda

private int lambda

Holds the value of LAMBDA_PARAM.

MINPTS_ID

public static final OptionID MINPTS_ID

OptionID for MINPTS_PARAM

MINPTS_PARAM

private final IntParameter MINPTS_PARAM

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

minpts

protected int minpts

Holds the value of MINPTS_PARAM.

resultList

private List<List<Integer>> resultList

Holds a list of clusters found.

noise

private Set<Integer> noise

Holds a set of noise.

processedIDs

private Set<Integer> processedIDs

Holds a set of processed ids.

innerDistanceFunction

private DistanceFunction<V extends NumberVector<V,?>,DoubleDistance> innerDistanceFunction

The inner distance function.

Constructor Detail

ProjectedDBSCAN

public ProjectedDBSCAN(Parameterization config)

Constructor, adhering to Parameterizable

Parameters:

config - Parameterization

Method Detail

runInTime

protected Clustering<Model> runInTime(Database<V> database)
                               throws IllegalStateException

Description copied from class: AbstractAlgorithm

The run method encapsulated in measure of runtime. An extending class needs not to take care of runtime itself.

Specified by:

runInTime in class AbstractAlgorithm<V extends NumberVector<V,?>,Clustering<Model>>

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm

Throws:

IllegalStateException - if the algorithm has not been initialized properly (e.g. the setParameters(String[]) method has been failed to be called).

expandCluster

protected void expandCluster(Database<V> database,
                             Integer startObjectID,
                             FiniteProgress objprog,
                             IndefiniteProgress clusprog)

ExpandCluster function of DBSCAN.

Parameters:

database - the database to run the algorithm on

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

objprog - the progress object for logging the current status

preprocessorClass

public abstract Class<?> preprocessorClass()

Returns the class actually used as VarianceAnalysisPreprocessor.

Returns:

the class actually used as VarianceAnalysisPreprocessor