tutorial.clustering

Class NaiveAgglomerativeHierarchicalClustering1<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.NaiveAgglomerativeHierarchicalClustering1<O,D>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, Parameterizable

public class NaiveAgglomerativeHierarchicalClustering1<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 first step, where we implement it with single linkage only, and extract a fixed number of clusters. The follow up variants will be made more flexible. 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	NaiveAgglomerativeHierarchicalClustering1.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 class de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
NaiveAgglomerativeHierarchicalClustering1(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

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

Class logger

numclusters

int numclusters

Threshold, how many clusters to extract.

Constructor Detail

NaiveAgglomerativeHierarchicalClustering1

public NaiveAgglomerativeHierarchicalClustering1(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

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