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

Class ByModelClustering

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByModelClustering

All Implemented Interfaces:

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

@Title(value="Clustering by model")
@Description(value="Cluster points by a (pre-assigned!) model. For comparing results with a reference clustering.")
public class ByModelClustering
extends AbstractAlgorithm<Clustering<Model>>
implements ClusteringAlgorithm<Clustering<Model>>

Pseudo clustering using annotated models. This "algorithm" puts elements into the same cluster when they agree in their model. I.e. it just uses a predefined clustering, and is mostly useful for testing and evaluation (e.g. comparing the result of a real algorithm to the reference result / golden standard used by the generator).

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ByModelClustering.Parameterizer Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG The logger for this class.
static OptionID	NOISE_ID Pattern to recognize noise clusters with
private Pattern	noisepattern Holds the value of NOISE_ID.

Constructor Summary

Constructors

Constructor and Description
ByModelClustering() Constructor without parameters
ByModelClustering(Pattern noisepattern) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
Clustering<Model>	run(Relation<Model> relation) Run the actual clustering algorithm.

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

The logger for this class.

NOISE_ID

public static final OptionID NOISE_ID

Pattern to recognize noise clusters with

noisepattern

private Pattern noisepattern

Holds the value of NOISE_ID.

Constructor Detail

ByModelClustering

public ByModelClustering(Pattern noisepattern)

Constructor.

Parameters:

noisepattern - Noise pattern

ByModelClustering

public ByModelClustering()

Constructor without parameters

Method Detail

run

public Clustering<Model> run(Relation<Model> relation)

Run the actual clustering algorithm.

Parameters:

relation - The data input we use

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