de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Class RepresentativeUncertainClustering

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<Clustering<Model>>

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<Model>>

@Reference(authors="Andreas Z\u00fcfle, Tobias Emrich, Klaus Arthur Schmid, Nikos Mamoulis, Arthur Zimek, Mathias Renz",
           title="Representative clustering of uncertain data",
           booktitle="Proc. 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
           url="http://dx.doi.org/10.1145/2623330.2623725")
public class RepresentativeUncertainClustering
extends AbstractAlgorithm<Clustering<Model>>
implements ClusteringAlgorithm<Clustering<Model>>

Representative clustering of uncertain data. This algorithm clusters uncertain data by repeatedly sampling a possible world, then running a traditional clustering algorithm on this sample. The resulting "possible" clusterings are then clustered themselves, using a clustering similarity measure. This yields a number of representatives for the set of all possible worlds. Reference:

Andreas Züfle, Tobias Emrich, Klaus Arthur Schmid, Nikos Mamoulis, Arthur Zimek, Mathias Renz
Representative clustering of uncertain data
In Proc. 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Author:

Alexander Koos, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	RepresentativeUncertainClustering.Parameterizer Parameterization class.
static class	RepresentativeUncertainClustering.RepresentativenessEvaluation Representativeness evaluation result.

Field Summary

Fields

Modifier and Type	Field and Description
protected double	alpha Alpha parameter for confidence.
protected ClusteringDistanceSimilarityFunction	distance Distance function for clusterings.
protected boolean	keep Keep all samples (not only the representative results)
private static Logging	LOG Initialize a Logger.
protected ClusteringAlgorithm<?>	metaAlgorithm The algorithm for meta-clustering.
protected int	numsamples How many clusterings shall be made for aggregation.
protected RandomFactory	random Random factory for sampling.
protected ClusteringAlgorithm<?>	samplesAlgorithm The algorithm to be wrapped and run.

Constructor Summary

Constructors

Constructor and Description
RepresentativeUncertainClustering(ClusteringDistanceSimilarityFunction distance, ClusteringAlgorithm<?> metaAlgorithm, ClusteringAlgorithm<?> samplesAlgorithm, int numsamples, RandomFactory random, double alpha, boolean keep) Constructor, quite trivial.

Method Summary

Methods

Modifier and Type	Method and Description
private double	computeConfidence(int support, int samples) Estimate the confidence probability of a clustering.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
Clustering<?>	run(Database database, Relation<? extends UncertainObject> relation) This run method will do the wrapping.
protected Clustering<?>	runClusteringAlgorithm(ResultHierarchy hierarchy, Result parent, DBIDs ids, DataStore<DoubleVector> store, int dim, String title) Run a clustering algorithm on a single instance.

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

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

LOG

private static final Logging LOG

Initialize a Logger.

distance

protected ClusteringDistanceSimilarityFunction distance

Distance function for clusterings.

metaAlgorithm

protected ClusteringAlgorithm<?> metaAlgorithm

The algorithm for meta-clustering.

samplesAlgorithm

protected ClusteringAlgorithm<?> samplesAlgorithm

The algorithm to be wrapped and run.

numsamples

protected int numsamples

How many clusterings shall be made for aggregation.

random

protected RandomFactory random

Random factory for sampling.

alpha

protected double alpha

Alpha parameter for confidence.

keep

protected boolean keep

Keep all samples (not only the representative results)

Constructor Detail

RepresentativeUncertainClustering

public RepresentativeUncertainClustering(ClusteringDistanceSimilarityFunction distance,
                                 ClusteringAlgorithm<?> metaAlgorithm,
                                 ClusteringAlgorithm<?> samplesAlgorithm,
                                 int numsamples,
                                 RandomFactory random,
                                 double alpha,
                                 boolean keep)

Constructor, quite trivial.

Parameters:

distance - Distance function for meta clustering

metaAlgorithm - Meta clustering algorithm

samplesAlgorithm - Primary clustering algorithm

numsamples - Number of samples

alpha - Alpha confidence

keep - Keep all samples (not only the representative results).

Method Detail

run

public Clustering<?> run(Database database,
                Relation<? extends UncertainObject> relation)

This run method will do the wrapping. Its called from AbstractAlgorithm.run(Database) and performs the call to the algorithms particular run method as well as the storing and comparison of the resulting Clusterings.

Parameters:

database - Database

relation - Data relation of uncertain objects

Returns:

Clustering result

computeConfidence

private double computeConfidence(int support,
                       int samples)

Estimate the confidence probability of a clustering.

Parameters:

support - Number of supporting samples

samples - Total samples

Returns:

Probability

runClusteringAlgorithm

protected Clustering<?> runClusteringAlgorithm(ResultHierarchy hierarchy,
                                   Result parent,
                                   DBIDs ids,
                                   DataStore<DoubleVector> store,
                                   int dim,
                                   String title)

Run a clustering algorithm on a single instance.

Parameters:

parent - Parent result to attach to

ids - Object IDs to process

store - Input data

dim - Dimensionality

title - Title of relation

Returns:

Clustering result

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<Clustering<Model>>

Returns:

Type restriction

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<Model>>

Returns:

the static logger