de.lmu.ifi.dbs.elki.algorithm.clustering.subspace
Class PROCLUS<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.subspace.ProjectedClustering<V>
                  de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PROCLUS<V>

Type Parameters:

V - the type of Realvector handled by this Algorithm

All Implemented Interfaces:

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

public class PROCLUS<V extends RealVector<V,?>>
extends ProjectedClustering<V>

PROCLUS provides the PROCLUS algorithm.

Author:

Elke Achtert

Nested Class Summary

private class

PROCLUS.Cluster Encapsulates the attributes of a cluster.

Field Summary

private int	m_i Holds m_i.
private IntParameter	M_I_PARAM Parameter to specify the the multiplier for the initial number of medoids, must be an integer greater than 0.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.ProjectedClustering

K_I_ID, K_ID, L_ID

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

PROCLUS()
Adds the parameter M_I_PARAM to the option handler additionally to the parameters provided by super-classes.

Method Summary

private Map<Integer,PROCLUS.Cluster>	assignPoints(Map<Integer,Set<Integer>> dimensions, Database<V> database) Assigns the objects to the clusters.
private double	avgDistance(V centroid, Set<Integer> objectIDs, Database<V> database, int dimension) Computes the average distance of the objects to the centroid along the specified dimension.
private Set<Integer>	computeBadMedoids(Map<Integer,PROCLUS.Cluster> clusters, int threshold) Computes the bad medoids, where the medoid of a cluster with less than the specified threshold of objkects is bad.
private Set<Integer>	computeM_current(Set<Integer> m, Set<Integer> m_best, Set<Integer> m_bad)
private double	evaluateClusters(Map<Integer,PROCLUS.Cluster> clusters, Map<Integer,Set<Integer>> dimensions, Database<V> database) Evaluates the quality of the clusters.
private Map<Integer,Set<Integer>>	findDimensions(Set<Integer> medoids, Database<V> database) Determines the set of correlated dimensions for each medoid in the specified medoid set.
Description	getDescription() Returns a description of the algorithm.
private Map<Integer,List<QueryResult<DoubleDistance>>>	getLocalities(Set<Integer> m_c, Database<V> database)
private Set<Integer>	greedy(Set<Integer> sampleSet, int m) Returns a piercing set of k medoids from the specified sample set.
private Set<Integer>	initialSet(Set<Integer> sampleSet, int k) Returns a set of k elements from the specified sample set.
private double	manhattanSegmentalDistance(V o1, V o2, Set<Integer> dimensions) Returns the Manhattan segmental distance between o1 and o2 realtive to the specified dimensions.
protected void	runInTime(Database<V> database) The run method encapsulated in measure of runtime.
String[]	setParameters(String[] args) Sets the parameters k and l additionally to the parameters set by the super-class' method.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.ProjectedClustering

getDistanceFunction, getK_i, getK, getL, getResult, setResult

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, getAttributeSettings, 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

run, setTime, setVerbose

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

checkGlobalParameterConstraints, description, getAttributeSettings, getParameters, getPossibleOptions, inlineDescription

Field Detail

M_I_PARAM

private final IntParameter M_I_PARAM

Parameter to specify the the multiplier for the initial number of medoids, must be an integer greater than 0.

Default value: 10

Key: -proclus.mi

m_i

private int m_i

Holds m_i.

Constructor Detail

PROCLUS

public PROCLUS()

Adds the parameter M_I_PARAM to the option handler additionally to the parameters provided by super-classes.

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(de.lmu.ifi.dbs.elki.database.Database)

getDescription

public Description getDescription()

Description copied from interface: Algorithm

Returns a description of the algorithm.

Returns:

a description of the algorithm

See Also:

Algorithm.getDescription()

setParameters

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

Sets the parameters k and l additionally to the parameters set by the super-class' method. Both k and l are required parameters.

Specified by:

setParameters in interface Parameterizable

Overrides:

setParameters in class ProjectedClustering<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[])

greedy

private Set<Integer> greedy(Set<Integer> sampleSet,
                            int m)

Returns a piercing set of k medoids from the specified sample set.

Parameters:

sampleSet - the sample set

m - the number of medoids to be returned

Returns:

a piercing set of m medoids from the specified sample set

initialSet

private Set<Integer> initialSet(Set<Integer> sampleSet,
                                int k)

Returns a set of k elements from the specified sample set.

Parameters:

sampleSet - the sample set

k - the number of samples to be returned

Returns:

a set of k elements from the specified sample set

computeM_current

private Set<Integer> computeM_current(Set<Integer> m,
                                      Set<Integer> m_best,
                                      Set<Integer> m_bad)

getLocalities

private Map<Integer,List<QueryResult<DoubleDistance>>> getLocalities(Set<Integer> m_c,
                                                                     Database<V> database)

findDimensions

private Map<Integer,Set<Integer>> findDimensions(Set<Integer> medoids,
                                                 Database<V> database)

Determines the set of correlated dimensions for each medoid in the specified medoid set.

Parameters:

medoids - the set of medoids

database - the database containing the objects

Returns:

the set of correlated dimensions for each medoid in the specified medoid set

assignPoints

private Map<Integer,PROCLUS.Cluster> assignPoints(Map<Integer,Set<Integer>> dimensions,
                                                  Database<V> database)

Assigns the objects to the clusters.

Parameters:

dimensions - set of correlated dimensions for each medoid of the cluster

database - the database containing the objects

Returns:

the assignments of the object to the clusters

manhattanSegmentalDistance

private double manhattanSegmentalDistance(V o1,
                                          V o2,
                                          Set<Integer> dimensions)

Returns the Manhattan segmental distance between o1 and o2 realtive to the specified dimensions.

Parameters:

o1 - the first object

o2 - the second object

dimensions - the dimensions to be considered

Returns:

the Manhattan segmental distance between o1 and o2 realtive to the specified dimensions

evaluateClusters

private double evaluateClusters(Map<Integer,PROCLUS.Cluster> clusters,
                                Map<Integer,Set<Integer>> dimensions,
                                Database<V> database)

Evaluates the quality of the clusters.

Parameters:

clusters - the clusters to be evaluated

dimensions - the dimensions associated with each cluster

database - the database holding the objects

Returns:

a measure for the cluster quality

avgDistance

private double avgDistance(V centroid,
                           Set<Integer> objectIDs,
                           Database<V> database,
                           int dimension)

Computes the average distance of the objects to the centroid along the specified dimension.

Parameters:

centroid - the centroid

objectIDs - the set of objects ids

database - the database holding the objects

dimension - the dimension for which the average distance is computed

Returns:

the average distance of the objects to the centroid along the specified dimension

computeBadMedoids

private Set<Integer> computeBadMedoids(Map<Integer,PROCLUS.Cluster> clusters,
                                       int threshold)

Computes the bad medoids, where the medoid of a cluster with less than the specified threshold of objkects is bad.

Parameters:

clusters - the clusters

threshold - the threshold

Returns:

the bad medoids