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

Class EvaluateSilhouette<O>

java.lang.Object

de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateSilhouette<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Evaluator, ResultProcessor

@Reference(authors="P. J. Rousseeuw",
           title="Silhouettes: A graphical aid to the interpretation and validation of cluster analysis",
           booktitle="Journal of Computational and Applied Mathematics, Volume 20",
           url="http://dx.doi.org/10.1016%2F0377-0427%2887%2990125-7")
public class EvaluateSilhouette<O>
extends Object
implements Evaluator

Compute the silhouette of a data set. Reference:

P. J. Rousseeuw
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
In: Journal of Computational and Applied Mathematics Volume 20, November 1987

TODO: keep all silhouette values, and allow visualization!

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	EvaluateSilhouette.Parameterizer<O> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private DistanceFunction<? super O>	distance Distance function to use.
private String	key Key for logging statistics.
private static Logging	LOG Logger for debug output.
private boolean	mergenoise Keep noise "clusters" merged, instead of breaking them into singletons.

Constructor Summary

Constructors

Constructor and Description
EvaluateSilhouette(DistanceFunction<? super O> distance, boolean mergenoise) Constructor.

Method Summary

Modifier and Type	Method and Description
void	evaluateClustering(Database db, Relation<O> rel, DistanceQuery<O> dq, Clustering<?> c) Evaluate a single clustering.
void	processNewResult(HierarchicalResult baseResult, Result result) 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.

mergenoise

private boolean mergenoise

Keep noise "clusters" merged, instead of breaking them into singletons.

distance

private DistanceFunction<? super O> distance

Distance function to use.

key

private String key

Key for logging statistics.

Constructor Detail

EvaluateSilhouette

public EvaluateSilhouette(DistanceFunction<? super O> distance,
                          boolean mergenoise)

Constructor.

Parameters:

distance - Distance function

mergenoise - Flag to treat noise as clusters, instead of breaking them into singletons.

Method Detail

evaluateClustering

public void evaluateClustering(Database db,
                               Relation<O> rel,
                               DistanceQuery<O> dq,
                               Clustering<?> c)

Evaluate a single clustering.

Parameters:

db - Database

rel - Data relation

dq - Distance query

c - Clustering

processNewResult

public void processNewResult(HierarchicalResult baseResult,
                             Result result)

Description copied from interface: ResultProcessor

Process a result.

Specified by:

processNewResult in interface ResultProcessor

Parameters:

baseResult - The base of the result tree.

result - Newly added result subtree.