Package elki.index.preprocessed.knn

Class RandomSampleKNNPreprocessor<O>

java.lang.Object

elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor<O>

elki.index.preprocessed.knn.RandomSampleKNNPreprocessor<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Index, KNNIndex<O>

@Reference(authors="Arthur Zimek, Matthew Gaudet, Ricardo J. G. B. Campello, J\u00f6rg Sander",
           title="Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles",
           booktitle="Proc. 19th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, KDD \'13",
           url="https://doi.org/10.1145/2487575.2487676",
           bibkey="DBLP:conf/kdd/ZimekGCS13")
public class RandomSampleKNNPreprocessor<O>
extends AbstractMaterializeKNNPreprocessor<O>

Class that computed the kNN only on a random sample.

Used in:

Arthur Zimek, Matthew Gaudet, Ricardo J. G. B. Campello, Jörg Sander
Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles
Proc. 19th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining KDD'13

Since:

0.5.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	RandomSampleKNNPreprocessor.Factory<O>	The parameterizable factory.

Field Summary

Fields

Modifier and Type	Field	Description
private static Logging	LOG	Logger
private RandomFactory	rnd	Random generator
private double	share	Relative share of objects to get

Fields inherited from class elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor

distance, distanceQuery, k, relation, storage

Constructor Summary

Constructors

Constructor	Description
RandomSampleKNNPreprocessor(Relation<O> relation, Distance<? super O> distance, int k, double share, RandomFactory rnd)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected Logging	getLogger()	Get the classes static logger.
protected void	preprocess()	Perform the preprocessing step.

Methods inherited from class elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor

createStorage, get, getDistanceQuery, getK, initialize, kNNByDBID, kNNByObject

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.index.Index

logStatistics

Field Detail

LOG

private static final Logging LOG

Logger

share

private final double share

Relative share of objects to get

rnd

private final RandomFactory rnd

Random generator

Constructor Detail

RandomSampleKNNPreprocessor

public RandomSampleKNNPreprocessor(Relation<O> relation,
                                   Distance<? super O> distance,
                                   int k,
                                   double share,
                                   RandomFactory rnd)

Constructor.

Parameters:

relation - Relation to index

distance - distance function

k - k

share - Relative share

rnd - Random generator

Method Detail

preprocess

protected void preprocess()

Description copied from class: AbstractMaterializeKNNPreprocessor

Perform the preprocessing step.

Specified by:

preprocess in class AbstractMaterializeKNNPreprocessor<O>

getLogger

protected Logging getLogger()

Description copied from class: AbstractMaterializeKNNPreprocessor

Get the classes static logger.

Specified by:

getLogger in class AbstractMaterializeKNNPreprocessor<O>

Returns:

Logger