Package elki.algorithm

Class KNNJoin

java.lang.Object

elki.algorithm.KNNJoin

All Implemented Interfaces:

Algorithm

@Title("K-Nearest Neighbor Join")
@Description("Algorithm to find the k-nearest neighbors of each object in a spatial database")
@Priority(-10)
public class KNNJoin
extends java.lang.Object
implements Algorithm

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. It does currently not yet use the tree structure for pruning.

TODO: exploit the tree structure.

Since:

0.1

Author:

Elke Achtert, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	KNNJoin.Par	Parameterization class.
private static class	KNNJoin.Task	Task in the processing queue.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
protected SpatialPrimitiveDistance<?>	distance	Distance function used.
protected int	k	The k parameter.
private static Logging	LOG	The logger for this class.

Constructor Summary

Constructors

Constructor	Description
KNNJoin(SpatialPrimitiveDistance<?> distance, int k)	Constructor.

Method Summary

Modifier and Type	Method	Description
Relation<KNNList>	autorun(Database database)	Try to auto-run the algorithm on a database by calling a method called run, with an optional Database first, and with data relations as specified by Algorithm.getInputTypeRestriction().
private double	computeStopDistance(java.util.List<KNNHeap> heaps)	Compute the maximum stop distance.
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.
private java.util.List<KNNHeap>	initHeaps(SpatialPrimitiveDistance<?> distFunction, AbstractRStarTreeNode<?,?> pr)	Initialize the heaps.
private void	processDataPages(SpatialPrimitiveDistance<?> df, java.util.List<KNNHeap> pr_heaps, java.util.List<KNNHeap> ps_heaps, AbstractRStarTreeNode<?,?> pr, AbstractRStarTreeNode<?,?> ps)	Processes the two data pages pr and ps and determines the k-nearest neighbors of pr in ps.
Relation<KNNList>	run(Relation<? extends SpatialComparable> relation)	Joins in the given spatial database to each object its k-nearest neighbors.
WritableDataStore<KNNList>	run(Relation<? extends SpatialComparable> relation, DBIDs ids)	Inner run method.
WritableDataStore<KNNList>	run(AbstractRStarTree<?,?,?> idx, DBIDs ids)	Inner run method.

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

The logger for this class.

distance

protected SpatialPrimitiveDistance<?> distance

Distance function used.

k

protected int k

The k parameter.

Constructor Detail

KNNJoin

public KNNJoin(SpatialPrimitiveDistance<?> distance,
               int k)

Constructor.

Parameters:

distance - Distance function

k - k parameter

Method Detail

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from interface: Algorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Returns:

Type restriction

autorun

public Relation<KNNList> autorun(Database database)

Description copied from interface: Algorithm

Try to auto-run the algorithm on a database by calling a method called run, with an optional Database first, and with data relations as specified by Algorithm.getInputTypeRestriction().

Specified by:

autorun in interface Algorithm

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm

run

public Relation<KNNList> run(Relation<? extends SpatialComparable> relation)

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

Parameters:

relation - Relation to process

Returns:

result

run

public WritableDataStore<KNNList> run(Relation<? extends SpatialComparable> relation,
                                      DBIDs ids)

Inner run method. This returns a double store, and is used by KNNJoinMaterializeKNNPreprocessor

Parameters:

relation - Data relation

ids - Object IDs

Returns:

Data store

run

public WritableDataStore<KNNList> run(AbstractRStarTree<?,?,?> idx,
                                      DBIDs ids)

Inner run method. This returns a double store, and is used by KNNJoinMaterializeKNNPreprocessor

Parameters:

idx - Index to process

ids - Object IDs

Returns:

Data store

initHeaps

private java.util.List<KNNHeap> initHeaps(SpatialPrimitiveDistance<?> distFunction,
                                          AbstractRStarTreeNode<?,?> pr)

Initialize the heaps.

Parameters:

distFunction - Distance function

pr - Node to initialize for

Returns:

List of heaps

processDataPages

private void processDataPages(SpatialPrimitiveDistance<?> df,
                              java.util.List<KNNHeap> pr_heaps,
                              java.util.List<KNNHeap> ps_heaps,
                              AbstractRStarTreeNode<?,?> pr,
                              AbstractRStarTreeNode<?,?> 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(java.util.List<KNNHeap> heaps)

Compute the maximum stop distance.

Parameters:

heaps - Heaps list

Returns:

the k-nearest neighbor distance of pr in ps