Package elki.clustering.kmeans
Class BisectingKMeans<V extends NumberVector,M extends MeanModel>
- java.lang.Object
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- elki.clustering.kmeans.BisectingKMeans<V,M>
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- Type Parameters:
V
- Vector typeM
- Model type
- All Implemented Interfaces:
Algorithm
,ClusteringAlgorithm<Clustering<M>>
,KMeans<V,M>
@Reference(authors="M. Steinbach, G. Karypis, V. Kumar", title="A Comparison of Document Clustering Techniques", booktitle="KDD workshop on text mining. Vol. 400. No. 1", url="http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf", bibkey="conf/kdd/SteinbachKK00") public class BisectingKMeans<V extends NumberVector,M extends MeanModel> extends java.lang.Object implements KMeans<V,M>
The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.Reference:
M. Steinbach, G. Karypis, V. Kumar
A Comparison of Document Clustering Techniques
KDD workshop on text mining. Vol. 400. No. 1- Since:
- 0.6.0
- Author:
- Stephan Baier
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Nested Class Summary
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Nested classes/interfaces inherited from interface elki.Algorithm
Algorithm.Utils
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Field Summary
Fields Modifier and Type Field Description private KMeans<V,M>
innerkMeans
Variant of kMeans for the bisecting step.private int
k
Desired value of k.private static Logging
LOG
The logger for this class.-
Fields inherited from interface elki.clustering.kmeans.KMeans
DISTANCE_FUNCTION_ID, INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID
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Constructor Summary
Constructors Constructor Description BisectingKMeans(int k, KMeans<V,M> innerkMeans)
Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description NumberVectorDistance<? super V>
getDistance()
Returns the distance.TypeInformation[]
getInputTypeRestriction()
Get the input type restriction used for negotiating the data query.Clustering<M>
run(Relation<V> relation)
Run the clustering algorithm.void
setDistance(NumberVectorDistance<? super V> distance)
Set the distance function to use.void
setInitializer(KMeansInitialization init)
Set the initialization method.void
setK(int k)
Set the value of k.-
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface elki.clustering.ClusteringAlgorithm
autorun
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Method Detail
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getInputTypeRestriction
public TypeInformation[] getInputTypeRestriction()
Description copied from interface:Algorithm
Get the input type restriction used for negotiating the data query.- Specified by:
getInputTypeRestriction
in interfaceAlgorithm
- Returns:
- Type restriction
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run
public Clustering<M> run(Relation<V> relation)
Description copied from interface:KMeans
Run the clustering algorithm.
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getDistance
public NumberVectorDistance<? super V> getDistance()
Description copied from interface:KMeans
Returns the distance.- Specified by:
getDistance
in interfaceKMeans<V extends NumberVector,M extends MeanModel>
- Returns:
- the distance
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setK
public void setK(int k)
Description copied from interface:KMeans
Set the value of k. Needed for some types of nested k-means.
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setDistance
public void setDistance(NumberVectorDistance<? super V> distance)
Description copied from interface:KMeans
Set the distance function to use.- Specified by:
setDistance
in interfaceKMeans<V extends NumberVector,M extends MeanModel>
- Parameters:
distance
- Distance function.
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setInitializer
public void setInitializer(KMeansInitialization init)
Description copied from interface:KMeans
Set the initialization method.- Specified by:
setInitializer
in interfaceKMeans<V extends NumberVector,M extends MeanModel>
- Parameters:
init
- Initialization method
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