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

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable
          de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<V>
              de.lmu.ifi.dbs.elki.algorithm.clustering.ProjectedDBSCAN<V>

Type Parameters:

V - the type of Realvector handled by this Algorithm

All Implemented Interfaces:

Algorithm<V>, Clustering<V>, Loggable, Parameterizable

Direct Known Subclasses:

FourC, PreDeCon

public abstract class ProjectedDBSCAN<V extends RealVector<V,?>>
extends AbstractAlgorithm<V>
implements Clustering<V>

Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.

Author:

Arthur Zimek

Field Summary

static OptionID	DBSCAN_EPSILON_ID OptionID for EPSILON_PARAM
protected ClassParameter<AbstractLocallyWeightedDistanceFunction>	DISTANCE_FUNCTION_PARAM Parameter to specify the distance function to determine the distance between database objects, must extend AbstractLocallyWeightedDistanceFunction.
private AbstractLocallyWeightedDistanceFunction<V,?>	distanceFunction The distance function.
static OptionID	DISTANCEFUNCTION_ID OptionID for DISTANCE_FUNCTION_PARAM
protected String	epsilon Epsilon.
private PatternParameter	EPSILON_PARAM Parameter to specify the maximum radius of the neighborhood to be considered, must be suitable to LocallyWeightedDistanceFunction.
private int	lambda Keeps lambda.
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 Minimum points.
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.
private Set<Integer>	processedIDs Holds a set of processed ids.
private ClustersPlusNoise<V>	result Provides the result of the algorithm.
private List<List<Integer>>	resultList Holds a list of clusters found.

Fields inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable

optionHandler

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

debug

Constructor Summary

protected

ProjectedDBSCAN() Provides the abstract algorithm for variance analysis based DBSCAN.

Method Summary

protected void	expandCluster(Database<V> database, Integer startObjectID, Progress progress) ExpandCluster function of DBSCAN.
List<AttributeSettings>	getAttributeSettings() Returns the settings of all options assigned to the option handler.
ClustersPlusNoise<V>	getResult() Returns the result of the algorithm.
abstract Class<?>	preprocessorClass() Returns the class actually used as VarianceAnalysisPreprocessor.
protected void	runInTime(Database<V> database) The run method encapsulated in measure of runtime.
String[]	setParameters(String[] args) Grabs all specified options from the option handler and sets the values for the flags AbstractAlgorithm.VERBOSE_FLAG and AbstractAlgorithm.TIME_FLAG.

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

description, isTime, isVerbose, run, setTime, setVerbose

Methods inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable

addOption, checkGlobalParameterConstraints, deleteOption, description, description, getParameters, getParameterValue, getPossibleOptions, inlineDescription, isSet, setParameters

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

debugFine, debugFiner, debugFinest, exception, message, progress, progress, progress, verbose, 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.Algorithm

getDescription, run, setTime, setVerbose

Methods inherited from interface de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable

checkGlobalParameterConstraints, description, getParameters, getPossibleOptions, inlineDescription

Field Detail

DISTANCEFUNCTION_ID

public static final OptionID DISTANCEFUNCTION_ID

OptionID for DISTANCE_FUNCTION_PARAM

DBSCAN_EPSILON_ID

public static final OptionID DBSCAN_EPSILON_ID

OptionID for EPSILON_PARAM

LAMBDA_ID

public static final OptionID LAMBDA_ID

OptionID for LAMBDA_PARAM

MINPTS_ID

public static final OptionID MINPTS_ID

OptionID for MINPTS_PARAM

DISTANCE_FUNCTION_PARAM

protected final ClassParameter<AbstractLocallyWeightedDistanceFunction> DISTANCE_FUNCTION_PARAM

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

Key: -projdbscan.distancefunction

Default value: EuklideanDistanceFunction

EPSILON_PARAM

private final PatternParameter EPSILON_PARAM

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

Key: -projdbscan.epsilon

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

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

epsilon

protected String epsilon

Epsilon.

minpts

protected int minpts

Minimum points.

lambda

private int lambda

Keeps lambda.

resultList

private List<List<Integer>> resultList

Holds a list of clusters found.

result

private ClustersPlusNoise<V extends RealVector<V,?>> result

Provides the result of the algorithm.

noise

private Set<Integer> noise

Holds a set of noise.

processedIDs

private Set<Integer> processedIDs

Holds a set of processed ids.

distanceFunction

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

The distance function.

Constructor Detail

ProjectedDBSCAN

protected ProjectedDBSCAN()

Provides the abstract algorithm for variance analysis based DBSCAN.

Method Detail

runInTime

protected void 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 RealVector<V,?>>

Parameters:

database - the database to run the algorithm on

Throws:

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

See Also:

AbstractAlgorithm.runInTime(Database)

expandCluster

protected void expandCluster(Database<V> database,
                             Integer startObjectID,
                             Progress progress)

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

progress - the progress object for logging the current status

setParameters

public String[] setParameters(String[] args)
                       throws ParameterException

Description copied from class: AbstractAlgorithm

Grabs all specified options from the option handler and sets the values for the flags AbstractAlgorithm.VERBOSE_FLAG and AbstractAlgorithm.TIME_FLAG. Any extending class should call this method first and return the returned array without further changes, but after setting further required parameters. An example for overwritting this method taking advantage from the previously (in superclasses) defined options would be:

 {
   String[] remainingParameters = super.setParameters(args);
   // set parameters for your class
   // for example like this:
   if(isSet(MY_PARAM_VALUE_PARAM))
   {
      myParamValue = getParameterValue(MY_PARAM_VALUE_PARAM);
   }
   .
   .
   .
   return remainingParameters;
   // or in case of attributes requesting parameters themselves
   // return parameterizableAttribbute.setParameters(remainingParameters);
 }
 

Specified by:

setParameters in interface Parameterizable

Overrides:

setParameters in class AbstractAlgorithm<V extends RealVector<V,?>>

Parameters:

args - parameters to set the attributes accordingly to

Returns:

String[] an array containing the unused parameters

Throws:

ParameterException - in case of wrong parameter-setting

See Also:

Parameterizable.setParameters(String[])

getAttributeSettings

public List<AttributeSettings> getAttributeSettings()

Description copied from class: AbstractParameterizable

Returns the settings of all options assigned to the option handler.

Specified by:

getAttributeSettings in interface Parameterizable

Overrides:

getAttributeSettings in class AbstractParameterizable

Returns:

the setting of the attributes of the parameterizable

See Also:

Parameterizable.getAttributeSettings()

preprocessorClass

public abstract Class<?> preprocessorClass()

Returns the class actually used as VarianceAnalysisPreprocessor.

Returns:

the class actually used as VarianceAnalysisPreprocessor

getResult

public ClustersPlusNoise<V> getResult()

Description copied from interface: Algorithm

Returns the result of the algorithm.

Specified by:

getResult in interface Algorithm<V extends RealVector<V,?>>

Specified by:

getResult in interface Clustering<V extends RealVector<V,?>>

Returns:

the result of the algorithm

See Also:

Algorithm.getResult()