Package elki.clustering.hierarchical.extraction

Class ClustersWithNoiseExtraction

java.lang.Object

elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<Model>>

@Reference(authors="Erich Schubert, Michael Gertz",
           title="Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding",
           booktitle="ArXiV preprint, 1708.03569",
           url="http://arxiv.org/abs/1708.03569",
           bibkey="DBLP:journals/corr/abs-1708-03569")
@Priority(206)
public class ClustersWithNoiseExtraction
extends java.lang.Object
implements ClusteringAlgorithm<Clustering<Model>>

Extraction of a given number of clusters with a minimum size, and noise.

This will execute the highest-most cut where we retain k clusters, each with a minimum size, plus noise (single points that would only merge afterwards). If no such cut can be found, it returns a result with a relaxed k.

You need to specify: A) the minimum size of a cluster (it does not make much sense to use 1 - then it will simply execute all but the last k merges) and B) the desired number of clusters with at least minSize elements each.

Reference:

Erich Schubert, Michael Gertz
Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding
ArXiV preprint, 1708.03569

TODO: Also provide representatives and last merge height for clusters.

Since:

0.7.5

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
protected class	ClustersWithNoiseExtraction.Instance	Instance for a single data set.
static class	ClustersWithNoiseExtraction.Par	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private HierarchicalClusteringAlgorithm	algorithm	Clustering algorithm to run to obtain the hierarchy.
private static Logging	LOG	Class logger.
private int	minClSize	Minimum cluster size.
private int	numCl	Minimum number of clusters.

Constructor Summary

Constructors

Constructor	Description
ClustersWithNoiseExtraction(HierarchicalClusteringAlgorithm algorithm, int numCl, int minClSize)	Constructor.

Method Summary

Modifier and Type	Method	Description
Clustering<Model>	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().
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.
Clustering<Model>	run(ClusterMergeHistory merges)	Process an existing result.

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.

numCl

private int numCl

Minimum number of clusters.

minClSize

private int minClSize

Minimum cluster size.

algorithm

private HierarchicalClusteringAlgorithm algorithm

Clustering algorithm to run to obtain the hierarchy.

Constructor Detail

ClustersWithNoiseExtraction

public ClustersWithNoiseExtraction(HierarchicalClusteringAlgorithm algorithm,
                                   int numCl,
                                   int minClSize)

Constructor.

Parameters:

algorithm - Algorithm to run

numCl - Number of clusters

minClSize - Minimum cluster size

Method Detail

autorun

public Clustering<Model> 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

Specified by:

autorun in interface ClusteringAlgorithm<Clustering<Model>>

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm

run

public Clustering<Model> run(ClusterMergeHistory merges)

Process an existing result.

Parameters:

merges - Existing result in pointer representation.

Returns:

Clustering

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