de.lmu.ifi.dbs.elki.algorithm

Class KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<V,DataStore<KNNList>>

de.lmu.ifi.dbs.elki.algorithm.KNNJoin<V,N,E>

Type Parameters:

V - the type of FeatureVector handled by this Algorithm

N - the type of node used in the spatial index structure

E - the type of entry used in the spatial node

All Implemented Interfaces:

Algorithm, DistanceBasedAlgorithm<V>

@Title(value="K-Nearest Neighbor Join")
@Description(value="Algorithm to find the k-nearest neighbors of each object in a spatial database")
public class KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>
extends AbstractDistanceBasedAlgorithm<V,DataStore<KNNList>>

Joins in a given spatial database to each object its k-nearest neighbors. This algorithm only supports spatial databases based on a spatial index structure. Since this method compares the MBR of every single leaf with every other leaf, it is essentially quadratic in the number of leaves, which may not be appropriate for large trees.

Author:

Elke Achtert, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KNNJoin.Parameterizer<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> Parameterization class.
private class	KNNJoin.Task Task in the processing queue.

Field Summary

Fields

Modifier and Type	Field and Description
(package private) int	k The k parameter.
private static Logging	LOG The logger for this class.

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
KNNJoin(DistanceFunction<? super V> distanceFunction, int k) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private double	computeStopDistance(List<KNNHeap> heaps) Compute the maximum stop distance.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
private List<KNNHeap>	initHeaps(SpatialPrimitiveDistanceFunction<V> distFunction, N pr) Initialize the heaps.
private void	processDataPages(SpatialPrimitiveDistanceFunction<? super V> df, List<KNNHeap> pr_heaps, List<KNNHeap> ps_heaps, N pr, N ps) Processes the two data pages pr and ps and determines the k-nearest neighbors of pr in ps.
WritableDataStore<KNNList>	run(Relation<V> relation) Joins in the given spatial database to each object its k-nearest neighbors.

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

getDistanceFunction

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.Algorithm

run

Field Detail

LOG

private static final Logging LOG

The logger for this class.

k

int k

The k parameter.

Constructor Detail

KNNJoin

public KNNJoin(DistanceFunction<? super V> distanceFunction,
       int k)

Constructor.

Parameters:

distanceFunction - Distance function

k - k parameter

Method Detail

run

public WritableDataStore<KNNList> run(Relation<V> relation)

Joins in the given spatial database to each object its k-nearest neighbors.

Parameters:

relation - Relation to process

Returns:

result

initHeaps

private List<KNNHeap> initHeaps(SpatialPrimitiveDistanceFunction<V> distFunction,
                      N pr)

Initialize the heaps.

Parameters:

distFunction - Distance function

pr - Node to initialize for

Returns:

List of heaps

processDataPages

private void processDataPages(SpatialPrimitiveDistanceFunction<? super V> df,
                    List<KNNHeap> pr_heaps,
                    List<KNNHeap> ps_heaps,
                    N pr,
                    N ps)

Processes the two data pages pr and ps and determines the k-nearest neighbors of pr in ps.

Parameters:

df - the distance function to use

pr - the first data page

ps - the second data page

pr_heaps - the knn lists for each data object

ps_heaps - the knn lists for each data object in ps

computeStopDistance

private double computeStopDistance(List<KNNHeap> heaps)

Compute the maximum stop distance.

Parameters:

heaps - Heaps list

Returns:

the k-nearest neighbor distance of pr in ps

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<DataStore<KNNList>>

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<DataStore<KNNList>>

Returns:

the static logger