de.lmu.ifi.dbs.elki.algorithm.clustering

Class SLINK<O,D extends Distance<D>>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,D,Result>

de.lmu.ifi.dbs.elki.algorithm.clustering.SLINK<O,D>

Type Parameters:

O - the type of DatabaseObject the algorithm is applied on

D - the type of Distance used

All Implemented Interfaces:

Algorithm, InspectionUtilFrequentlyScanned, Parameterizable

@Title(value="SLINK: Single Link Clustering")
@Description(value="Hierarchical clustering algorithm based on single-link connectivity.")
@Reference(authors="R. Sibson",
           title="SLINK: An optimally efficient algorithm for the single-link cluster method",
           booktitle="The Computer Journal 16 (1973), No. 1, p. 30-34.",
           url="http://dx.doi.org/10.1093/comjnl/16.1.30")
public class SLINK<O,D extends Distance<D>>
extends AbstractDistanceBasedAlgorithm<O,D,Result>

Efficient implementation of the Single-Link Algorithm SLINK of R. Sibson.

Reference: R. Sibson: SLINK: An optimally efficient algorithm for the single-link cluster method.
In: The Computer Journal 16 (1973), No. 1, p. 30-34.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
private static class	SLINK.CompareByLambda<D extends Distance<D>> Order a DBID collection by the lambda value.
static class	SLINK.Parameterizer<O,D extends Distance<D>> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private WritableDataStore<D>	lambda The values of the function Lambda of the pointer representation.
private static Logging	logger The logger for this class.
private Integer	minclusters Minimum number of clusters to extract
private WritableDataStore<DBID>	pi The values of the function Pi of the pointer representation.
static OptionID	SLINK_MINCLUSTERS_ID The minimum number of clusters to extract

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
SLINK(DistanceFunction<? super O,D> distanceFunction, Integer minclusters) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private Cluster<DendrogramModel<D>>	createParent(String name, Cluster<DendrogramModel<D>> leftChild, Cluster<DendrogramModel<D>> rightChild, D distance, ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier)
private Clustering<Model>	extractClusters_erich(DBIDs ids, DataStore<DBID> pi, DataStore<D> lambda, int minclusters) Extract all clusters from the pi-lambda-representation.
private Clustering<DendrogramModel<D>>	extractClusters(DBIDs ids, DataStore<DBID> pi, DataStore<D> lambda, int minclusters) Extract all clusters from the pi-lambda-representation.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
private Cluster<DendrogramModel<D>>	lastAncestor(Cluster<DendrogramModel<D>> cluster, ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier) Determines recursively the last ancestor of the specified cluster.
private DBID	lastObjectInCluster(DBID id, D stopdist, DataStore<DBID> pi, DataStore<D> lambda)
private Cluster<DendrogramModel<D>>	root(Map<DBID,ModifiableDBIDs> cluster_ids, Map<DBID,D> cluster_distances, DataStore<DBID> pi, DataStore<D> lambda, ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier, FiniteProgress progress)
Result	run(Database database, Relation<O> relation) Performs the SLINK algorithm on the given database.
private void	step1(DBID newID) First step: Initialize P(id) = id, L(id) = infinity.
private void	step2(DBID newID, DBIDs processedIDs, DistanceQuery<O,D> distFunc, WritableDataStore<D> m) Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.
private void	step3(DBID newID, DBIDs processedIDs, WritableDataStore<D> m) Third step: Determine the values for P and L
private void	step4(DBID newID, DBIDs processedIDs) Fourth step: Actualize the clusters if necessary

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

Field Detail

logger

private static final Logging logger

The logger for this class.

SLINK_MINCLUSTERS_ID

public static final OptionID SLINK_MINCLUSTERS_ID

The minimum number of clusters to extract

pi

private WritableDataStore<DBID> pi

The values of the function Pi of the pointer representation.

lambda

private WritableDataStore<D extends Distance<D>> lambda

The values of the function Lambda of the pointer representation.

minclusters

private Integer minclusters

Minimum number of clusters to extract

Constructor Detail

SLINK

public SLINK(DistanceFunction<? super O,D> distanceFunction,
     Integer minclusters)

Constructor.

Parameters:

distanceFunction - Distance function

minclusters - Minimum clusters to extract. Can be null

Method Detail

run

public Result run(Database database,
         Relation<O> relation)

Performs the SLINK algorithm on the given database.

step1

private void step1(DBID newID)

First step: Initialize P(id) = id, L(id) = infinity.

Parameters:

newID - the id of the object to be inserted into the pointer representation

step2

private void step2(DBID newID,
         DBIDs processedIDs,
         DistanceQuery<O,D> distFunc,
         WritableDataStore<D> m)

Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.

Parameters:

newID - the id of the object to be inserted into the pointer representation

processedIDs - the already processed ids

distFunc - Distance function to use

step3

private void step3(DBID newID,
         DBIDs processedIDs,
         WritableDataStore<D> m)

Third step: Determine the values for P and L

Parameters:

newID - the id of the object to be inserted into the pointer representation

processedIDs - the already processed ids

step4

private void step4(DBID newID,
         DBIDs processedIDs)

Fourth step: Actualize the clusters if necessary

Parameters:

newID - the id of the current object

processedIDs - the already processed ids

lastObjectInCluster

private DBID lastObjectInCluster(DBID id,
                       D stopdist,
                       DataStore<DBID> pi,
                       DataStore<D> lambda)

extractClusters

private Clustering<DendrogramModel<D>> extractClusters(DBIDs ids,
                                             DataStore<DBID> pi,
                                             DataStore<D> lambda,
                                             int minclusters)

Extract all clusters from the pi-lambda-representation.

Parameters:

ids - Object ids to process

pi - Pi store

lambda - Lambda store

minclusters - Minimum number of clusters to extract

Returns:

Hierarchical clustering

root

private Cluster<DendrogramModel<D>> root(Map<DBID,ModifiableDBIDs> cluster_ids,
                               Map<DBID,D> cluster_distances,
                               DataStore<DBID> pi,
                               DataStore<D> lambda,
                               ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier,
                               FiniteProgress progress)

lastAncestor

private Cluster<DendrogramModel<D>> lastAncestor(Cluster<DendrogramModel<D>> cluster,
                                       ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier)

Determines recursively the last ancestor of the specified cluster.

Parameters:

cluster - the child

hier - the cluster hierarchy

Returns:

the (currently) last ancestor

createParent

private Cluster<DendrogramModel<D>> createParent(String name,
                                       Cluster<DendrogramModel<D>> leftChild,
                                       Cluster<DendrogramModel<D>> rightChild,
                                       D distance,
                                       ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier)

extractClusters_erich

private Clustering<Model> extractClusters_erich(DBIDs ids,
                                      DataStore<DBID> pi,
                                      DataStore<D> lambda,
                                      int minclusters)

Extract all clusters from the pi-lambda-representation.

Parameters:

ids - Object ids to process

pi - Pi store

lambda - Lambda store

minclusters - Minimum number of clusters to extract

Returns:

Hierarchical clustering

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<Result>

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<Result>

Returns:

the static logger