tutorial.clustering

Class NaiveAgglomerativeHierarchicalClustering2<O,D extends NumberDistance<D,?>>

java.lang.Object

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

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

tutorial.clustering.NaiveAgglomerativeHierarchicalClustering2<O,D>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, DistanceBasedAlgorithm<O,D>, Parameterizable

public class NaiveAgglomerativeHierarchicalClustering2<O,D extends NumberDistance<D,?>>
extends AbstractDistanceBasedAlgorithm<O,D,Result>

This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps. This is the second step, where we increase the performance of the algorithm by using an improved linear memory layout instead of ragged arrays. This is the naive O(n^3) algorithm. See SLINK for a much faster algorithm (however, only for single-linkage).

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	NaiveAgglomerativeHierarchicalClustering2.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG Class logger
(package private) int	numclusters Threshold, how many clusters to extract.

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
NaiveAgglomerativeHierarchicalClustering2(DistanceFunction<? super O,D> distanceFunction, int numclusters) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
Result	run(Database db, Relation<O> relation) Run the algorithm
protected static int	triangleSize(int x) Compute the size of a complete x by x triangle (minus diagonal)

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

Class logger

numclusters

int numclusters

Threshold, how many clusters to extract.

Constructor Detail

NaiveAgglomerativeHierarchicalClustering2

public NaiveAgglomerativeHierarchicalClustering2(DistanceFunction<? super O,D> distanceFunction,
                                         int numclusters)

Constructor.

Parameters:

distanceFunction - Distance function to use

numclusters - Number of clusters

Method Detail

run

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

Run the algorithm

Parameters:

db - Database

relation - Relation

Returns:

Clustering hierarchy

triangleSize

protected static int triangleSize(int x)

Compute the size of a complete x by x triangle (minus diagonal)

Parameters:

x - Offset

Returns:

Size of complete triangle

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