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>

Implementation of the efficient 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.CompareByDoubleLambda Order a DBID collection by the lambda value.
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 static Logging	LOG The logger for this class.
private int	minclusters 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, int minclusters) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private Clustering<DendrogramModel<D>>	extractClusters(DBIDs ids, DBIDDataStore pi, DataStore<D> lambda, int minclusters) Extract all clusters from the pi-lambda-representation.
private Clustering<DendrogramModel<D>>	extractClustersDouble(DBIDs ids, DBIDDataStore pi, DoubleDistanceDataStore 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>>	makeCluster(DBIDRef lead, D depth, DBIDs members, ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier) Make the cluster for the given object
Result	run(Database database, Relation<O> relation) Performs the SLINK algorithm on the given database.
private void	step1(DBIDRef id, WritableDBIDDataStore pi, WritableDataStore<D> lambda) First step: Initialize P(id) = id, L(id) = infinity.
private void	step1double(DBIDRef id, WritableDBIDDataStore pi, WritableDoubleDistanceDataStore lambda) First step: Initialize P(id) = id, L(id) = infinity.
private void	step2(DBIDRef id, 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	step2double(DBIDRef id, DBIDs processedIDs, Relation<? extends O> relation, PrimitiveDoubleDistanceFunction<? super O> distFunc, WritableDoubleDistanceDataStore 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(DBIDRef id, WritableDBIDDataStore pi, WritableDataStore<D> lambda, DBIDs processedIDs, WritableDataStore<D> m) Third step: Determine the values for P and L
private void	step3double(DBIDRef id, WritableDBIDDataStore pi, WritableDoubleDistanceDataStore lambda, DBIDs processedIDs, WritableDoubleDistanceDataStore m) Third step: Determine the values for P and L
private void	step4(DBIDRef id, WritableDBIDDataStore pi, WritableDataStore<D> lambda, DBIDs processedIDs) Fourth step: Actualize the clusters if necessary
private void	step4double(DBIDRef id, WritableDBIDDataStore pi, WritableDoubleDistanceDataStore lambda, 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

LOG

private static final Logging LOG

The logger for this class.

minclusters

private int minclusters

Minimum number of clusters to extract

Constructor Detail

SLINK

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

Constructor.

Parameters:

distanceFunction - Distance function

minclusters - Minimum clusters to extract. Can be -1.

Method Detail

run

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

Performs the SLINK algorithm on the given database.

step1

private void step1(DBIDRef id,
         WritableDBIDDataStore pi,
         WritableDataStore<D> lambda)

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

Parameters:

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

pi - Pi data store

lambda - Lambda data store

step2

private void step2(DBIDRef id,
         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:

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

processedIDs - the already processed ids

m - Data store

distFunc - Distance function to use

step3

private void step3(DBIDRef id,
         WritableDBIDDataStore pi,
         WritableDataStore<D> lambda,
         DBIDs processedIDs,
         WritableDataStore<D> m)

Third step: Determine the values for P and L

Parameters:

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

pi - Pi data store

lambda - Lambda data store

processedIDs - the already processed ids

m - Data store

step4

private void step4(DBIDRef id,
         WritableDBIDDataStore pi,
         WritableDataStore<D> lambda,
         DBIDs processedIDs)

Fourth step: Actualize the clusters if necessary

Parameters:

id - the id of the current object

pi - Pi data store

lambda - Lambda data store

processedIDs - the already processed ids

step1double

private void step1double(DBIDRef id,
               WritableDBIDDataStore pi,
               WritableDoubleDistanceDataStore lambda)

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

Parameters:

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

pi - Pi data store

lambda - Lambda data store

step2double

private void step2double(DBIDRef id,
               DBIDs processedIDs,
               Relation<? extends O> relation,
               PrimitiveDoubleDistanceFunction<? super O> distFunc,
               WritableDoubleDistanceDataStore m)

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

Parameters:

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

processedIDs - the already processed ids

m - Data store

relation - Data relation

distFunc - Distance function to use

step3double

private void step3double(DBIDRef id,
               WritableDBIDDataStore pi,
               WritableDoubleDistanceDataStore lambda,
               DBIDs processedIDs,
               WritableDoubleDistanceDataStore m)

Third step: Determine the values for P and L

Parameters:

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

pi - Pi data store

lambda - Lambda data store

processedIDs - the already processed ids

m - Data store

step4double

private void step4double(DBIDRef id,
               WritableDBIDDataStore pi,
               WritableDoubleDistanceDataStore lambda,
               DBIDs processedIDs)

Fourth step: Actualize the clusters if necessary

Parameters:

id - the id of the current object

pi - Pi data store

lambda - Lambda data store

processedIDs - the already processed ids

extractClusters

private Clustering<DendrogramModel<D>> extractClusters(DBIDs ids,
                                             DBIDDataStore 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

extractClustersDouble

private Clustering<DendrogramModel<D>> extractClustersDouble(DBIDs ids,
                                                   DBIDDataStore pi,
                                                   DoubleDistanceDataStore 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

makeCluster

private Cluster<DendrogramModel<D>> makeCluster(DBIDRef lead,
                                      D depth,
                                      DBIDs members,
                                      ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier)

Make the cluster for the given object

Parameters:

lead - Leading object

depth - Linkage depth

members - Member objects

hier - Cluster hierarchy

Returns:

Cluster

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