de.lmu.ifi.dbs.elki.algorithm
Class DependencyDerivator<V extends RealVector<V,?>,D extends Distance<D>>

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<O,R>
              de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm<V,D,CorrelationAnalysisSolution<V>>
                  de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator<V,D>

Type Parameters:

V - the type of RealVector handled by this Algorithm

D - the type of Distance used by this Algorithm

All Implemented Interfaces:

Algorithm<V,CorrelationAnalysisSolution<V>>, Parameterizable

public class DependencyDerivator<V extends RealVector<V,?>,D extends Distance<D>>
extends DistanceBasedAlgorithm<V,D,CorrelationAnalysisSolution<V>>

Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.

Reference:
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, A. Zimek: Deriving Quantitative Dependencies for Correlation Clusters.
In Proc. 12th Int. Conf. on Knowledge Discovery and Data Mining (KDD '06), Philadelphia, PA 2006.

Author:

Arthur Zimek

Field Summary

static OptionID	DEPENDENCY_DERIVATOR_RANDOM_SAMPLE OptionID for RANDOM_SAMPLE_FLAG
NumberFormat	NF Number format for output of solution.
static OptionID	OUTPUT_ACCURACY_ID OptionID for OUTPUT_ACCURACY_PARAM
private IntParameter	OUTPUT_ACCURACY_PARAM Parameter to specify the threshold for output accuracy fraction digits, must be an integer equal to or greater than 0.
private PCAFilteredRunner<V,DoubleDistance>	pca Holds the object performing the pca.
private Flag	RANDOM_SAMPLE_FLAG Flag to use random sample (use knn query around centroid, if flag is not set).
static OptionID	SAMPLE_SIZE_ID OptionID for SAMPLE_SIZE_PARAM
private IntParameter	SAMPLE_SIZE_PARAM Optional parameter to specify the treshold for the size of the random sample to use, must be an integer greater than 0.
private Integer	sampleSize Holds the value of SAMPLE_SIZE_PARAM.
private CorrelationAnalysisSolution<V>	solution Holds the solution.

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

DISTANCE_FUNCTION_ID, DISTANCE_FUNCTION_PARAM

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, logger

Constructor Summary

DependencyDerivator()
Provides a dependency derivator, adding parameters OUTPUT_ACCURACY_PARAM, SAMPLE_SIZE_PARAM , and flag RANDOM_SAMPLE_FLAG to the option handler additionally to parameters of super class.

Method Summary

CorrelationAnalysisSolution<V>	generateModel(Database<V> db, Collection<Integer> ids) Runs the pca on the given set of IDs.
CorrelationAnalysisSolution<V>	generateModel(Database<V> db, Collection<Integer> ids, V centroidDV) Runs the pca on the given set of IDs and for the given centroid.
Description	getDescription() Returns a description of the algorithm.
CorrelationAnalysisSolution<V>	getResult() Returns the result of the algorithm.
CorrelationAnalysisSolution<V>	runInTime(Database<V> db) Computes quantitatively linear dependencies among the attributes of the given database based on a linear correlation PCA.
List<String>	setParameters(List<String> args) Calls the super method and sets additionally the values of the parameters OUTPUT_ACCURACY_PARAM and SAMPLE_SIZE_PARAM.

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

getDistanceFunction

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.utilities.optionhandling.AbstractParameterizable

addOption, addParameterizable, addParameterizable, checkGlobalParameterConstraints, collectOptions, getAttributeSettings, getParameters, rememberParametersExcept, removeOption, removeParameterizable, shortDescription

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.utilities.optionhandling.Parameterizable

checkGlobalParameterConstraints, collectOptions, getParameters, shortDescription

Field Detail

DEPENDENCY_DERIVATOR_RANDOM_SAMPLE

public static final OptionID DEPENDENCY_DERIVATOR_RANDOM_SAMPLE

OptionID for RANDOM_SAMPLE_FLAG

OUTPUT_ACCURACY_ID

public static final OptionID OUTPUT_ACCURACY_ID

OptionID for OUTPUT_ACCURACY_PARAM

OUTPUT_ACCURACY_PARAM

private final IntParameter OUTPUT_ACCURACY_PARAM

Parameter to specify the threshold for output accuracy fraction digits, must be an integer equal to or greater than 0.

Default value: 4

Key: -derivator.accuracy

SAMPLE_SIZE_ID

public static final OptionID SAMPLE_SIZE_ID

OptionID for SAMPLE_SIZE_PARAM

SAMPLE_SIZE_PARAM

private final IntParameter SAMPLE_SIZE_PARAM

Optional parameter to specify the treshold for the size of the random sample to use, must be an integer greater than 0.

Default value: the size of the complete dataset

Key: -derivator.sampleSize

sampleSize

private Integer sampleSize

Holds the value of SAMPLE_SIZE_PARAM.

RANDOM_SAMPLE_FLAG

private final Flag RANDOM_SAMPLE_FLAG

Flag to use random sample (use knn query around centroid, if flag is not set).

Key: -derivator.randomSample

pca

private PCAFilteredRunner<V extends RealVector<V,?>,DoubleDistance> pca

Holds the object performing the pca.

solution

private CorrelationAnalysisSolution<V extends RealVector<V,?>> solution

Holds the solution.

NF

public final NumberFormat NF

Number format for output of solution.

Constructor Detail

DependencyDerivator

public DependencyDerivator()

Provides a dependency derivator, adding parameters OUTPUT_ACCURACY_PARAM, SAMPLE_SIZE_PARAM , and flag RANDOM_SAMPLE_FLAG to the option handler additionally to parameters of super class.

Method Detail

getDescription

public Description getDescription()

Description copied from interface: Algorithm

Returns a description of the algorithm.

Returns:

a description of the algorithm

runInTime

public CorrelationAnalysisSolution<V> runInTime(Database<V> db)
                                                                 throws IllegalStateException

Computes quantitatively linear dependencies among the attributes of the given database based on a linear correlation PCA.

Specified by:

runInTime in class AbstractAlgorithm<V extends RealVector<V,?>,CorrelationAnalysisSolution<V extends RealVector<V,?>>>

Parameters:

db - the database to run this DependencyDerivator on

Returns:

the CorrelationAnalysisSolution computed by this DependencyDerivator

Throws:

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

generateModel

public CorrelationAnalysisSolution<V> generateModel(Database<V> db,
                                                    Collection<Integer> ids)

Runs the pca on the given set of IDs. The centroid is computed from the given ids.

Parameters:

db - the database

ids - the set of ids

Returns:

a matrix of equations describing the dependencies

generateModel

public CorrelationAnalysisSolution<V> generateModel(Database<V> db,
                                                    Collection<Integer> ids,
                                                    V centroidDV)

Runs the pca on the given set of IDs and for the given centroid.

Parameters:

db - the database

ids - the set of ids

centroidDV - the centroid

Returns:

a matrix of equations describing the dependencies

getResult

public CorrelationAnalysisSolution<V> getResult()

Description copied from interface: Algorithm

Returns the result of the algorithm.

Returns:

the result of the algorithm

setParameters

public List<String> setParameters(List<String> args)
                           throws ParameterException

Calls the super method and sets additionally the values of the parameters OUTPUT_ACCURACY_PARAM and SAMPLE_SIZE_PARAM. The remaining parameters are passed to the pca.

Specified by:

setParameters in interface Parameterizable

Overrides:

setParameters in class DistanceBasedAlgorithm<V extends RealVector<V,?>,D extends Distance<D>,CorrelationAnalysisSolution<V extends RealVector<V,?>>>

Parameters:

args - parameters to set the attributes accordingly to

Returns:

a list containing the unused parameters

Throws:

ParameterException - in case of wrong parameter-setting