de.lmu.ifi.dbs.elki.evaluation.clustering

Class EvaluateClustering

java.lang.Object

de.lmu.ifi.dbs.elki.evaluation.clustering.EvaluateClustering

All Implemented Interfaces:

Evaluator, ResultProcessor

@Alias(value="de.lmu.ifi.dbs.elki.evaluation.paircounting.EvaluatePairCountingFMeasure")
public class EvaluateClustering
extends java.lang.Object
implements Evaluator

Evaluate a clustering result by comparing it to an existing cluster label.

Since:

0.4.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	EvaluateClustering.Parameterizer Parameterization class.
static class	EvaluateClustering.ScoreResult Result object for outlier score judgements.

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG Logger for debug output.
private boolean	noiseSpecialHandling Apply special handling to noise "clusters".
private ClusteringAlgorithm<?>	referencealg Reference algorithm.
private boolean	selfPairing Use self-pairing in pair-counting measures

Constructor Summary

Constructors

Constructor and Description
EvaluateClustering(ClusteringAlgorithm<?> referencealg, boolean noiseSpecialHandling, boolean selfPairing) Constructor.

Method Summary

Modifier and Type	Method and Description
static double	evaluateRanking(ScoreEvaluation eval, Cluster<?> clus, DoubleDBIDList ranking) Evaluate given a cluster (of positive elements) and a scoring list.
protected void	evaluteResult(Database db, Clustering<?> c, Clustering<?> refc) Evaluate a clustering result.
private boolean	isReferenceResult(Clustering<?> t) Test if a clustering result is a valid reference result.
void	processNewResult(ResultHierarchy hier, Result newResult) Process a result.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

LOG

private static final Logging LOG

Logger for debug output.

referencealg

private ClusteringAlgorithm<?> referencealg

Reference algorithm.

noiseSpecialHandling

private boolean noiseSpecialHandling

Apply special handling to noise "clusters".

selfPairing

private boolean selfPairing

Use self-pairing in pair-counting measures

Constructor Detail

EvaluateClustering

public EvaluateClustering(ClusteringAlgorithm<?> referencealg,
                          boolean noiseSpecialHandling,
                          boolean selfPairing)

Constructor.

Parameters:

referencealg - Reference clustering

noiseSpecialHandling - Noise handling flag

selfPairing - Self-pairing flag

Method Detail

evaluateRanking

public static double evaluateRanking(ScoreEvaluation eval,
                                     Cluster<?> clus,
                                     DoubleDBIDList ranking)

Evaluate given a cluster (of positive elements) and a scoring list.

Parameters:

eval - Evaluation method

clus - Cluster object

ranking - Object ranking

Returns:

Score

processNewResult

public void processNewResult(ResultHierarchy hier,
                             Result newResult)

Description copied from interface: ResultProcessor

Process a result.

Specified by:

processNewResult in interface ResultProcessor

Parameters:

hier - The base of the result tree.

newResult - Newly added result subtree.

evaluteResult

protected void evaluteResult(Database db,
                             Clustering<?> c,
                             Clustering<?> refc)

Evaluate a clustering result.

Parameters:

db - Database

c - Clustering

refc - Reference clustering

isReferenceResult

private boolean isReferenceResult(Clustering<?> t)

Test if a clustering result is a valid reference result.

Parameters:

t - Clustering to test.

Returns:

true if it is considered to be a reference result.