Package elki.clustering.kmeans
Class MacQueenKMeans<V extends NumberVector>
- java.lang.Object
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- elki.clustering.kmeans.AbstractKMeans<V,KMeansModel>
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- elki.clustering.kmeans.MacQueenKMeans<V>
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- Type Parameters:
V
- vector type to use
- All Implemented Interfaces:
Algorithm
,ClusteringAlgorithm<Clustering<KMeansModel>>
,KMeans<V,KMeansModel>
@Title("k-Means (MacQueen Algorithm)") @Reference(authors="J. MacQueen", title="Some Methods for Classification and Analysis of Multivariate Observations", booktitle="5th Berkeley Symp. Math. Statist. Prob.", url="http://projecteuclid.org/euclid.bsmsp/1200512992", bibkey="conf/bsmsp/MacQueen67") public class MacQueenKMeans<V extends NumberVector> extends AbstractKMeans<V,KMeansModel>
The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.This implementation will by default iterate over the data set until convergence, although MacQueen likely only meant to do a single pass over the data, but the result quality improves with multiple passes.
Reference:
J. MacQueen
Some Methods for Classification and Analysis of Multivariate Observations
5th Berkeley Symp. Math. Statist. Prob.- Since:
- 0.1
- Author:
- Erich Schubert
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Nested Class Summary
Nested Classes Modifier and Type Class Description protected static class
MacQueenKMeans.Instance
Inner instance, storing state for a single data set.static class
MacQueenKMeans.Par<V extends NumberVector>
Parameterization class.-
Nested classes/interfaces inherited from interface elki.Algorithm
Algorithm.Utils
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Field Summary
Fields Modifier and Type Field Description private static Logging
LOG
The logger for this class.-
Fields inherited from class elki.clustering.kmeans.AbstractKMeans
distance, initializer, k, maxiter
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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 MacQueenKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)
Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description protected Logging
getLogger()
Get the (STATIC) logger for this class.Clustering<KMeansModel>
run(Relation<V> relation)
Run the clustering algorithm.-
Methods inherited from class elki.clustering.kmeans.AbstractKMeans
getDistance, getInputTypeRestriction, incrementalUpdateMean, initialMeans, means, minusEquals, nearestMeans, plusEquals, plusMinusEquals, setDistance, setInitializer, setK
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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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Field Detail
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LOG
private static final Logging LOG
The logger for this class.
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Constructor Detail
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MacQueenKMeans
public MacQueenKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)
Constructor.- Parameters:
distance
- distance functionk
- k parametermaxiter
- Maxiter parameterinitializer
- Initialization method
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Method Detail
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run
public Clustering<KMeansModel> run(Relation<V> relation)
Description copied from interface:KMeans
Run the clustering algorithm.- Parameters:
relation
- Relation to process.- Returns:
- Clustering result
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getLogger
protected Logging getLogger()
Description copied from class:AbstractKMeans
Get the (STATIC) logger for this class.- Specified by:
getLogger
in classAbstractKMeans<V extends NumberVector,KMeansModel>
- Returns:
- the static logger
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