T - type of the points to clusterKMeansPlusPlusClusterer instead@Deprecated public class KMeansPlusPlusClusterer<T extends Clusterable<T>> extends Object
| Modifier and Type | Class and Description | 
|---|---|
| static class  | KMeansPlusPlusClusterer.EmptyClusterStrategyDeprecated.  Strategies to use for replacing an empty cluster. | 
| Constructor and Description | 
|---|
| KMeansPlusPlusClusterer(Random random)Deprecated.  Build a clusterer. | 
| KMeansPlusPlusClusterer(Random random,
                       KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)Deprecated.  Build a clusterer. | 
| Modifier and Type | Method and Description | 
|---|---|
| List<Cluster<T>> | cluster(Collection<T> points,
       int k,
       int maxIterations)Deprecated.  Runs the K-means++ clustering algorithm. | 
| List<Cluster<T>> | cluster(Collection<T> points,
       int k,
       int numTrials,
       int maxIterationsPerTrial)Deprecated.  Runs the K-means++ clustering algorithm. | 
public KMeansPlusPlusClusterer(Random random)
The default strategy for handling empty clusters that may appear during algorithm iterations is to split the cluster with largest distance variance.
random - random generator to use for choosing initial centerspublic KMeansPlusPlusClusterer(Random random, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)
random - random generator to use for choosing initial centersemptyStrategy - strategy to use for handling empty clusters that
 may appear during algorithm iterationspublic List<Cluster<T>> cluster(Collection<T> points, int k, int numTrials, int maxIterationsPerTrial) throws MathIllegalArgumentException, ConvergenceException
points - the points to clusterk - the number of clusters to split the data intonumTrials - number of trial runsmaxIterationsPerTrial - the maximum number of iterations to run the algorithm
     for at each trial run.  If negative, no maximum will be usedMathIllegalArgumentException - if the data points are null or the number
     of clusters is larger than the number of data pointsConvergenceException - if an empty cluster is encountered and the
 emptyStrategy is set to ERRORpublic List<Cluster<T>> cluster(Collection<T> points, int k, int maxIterations) throws MathIllegalArgumentException, ConvergenceException
points - the points to clusterk - the number of clusters to split the data intomaxIterations - the maximum number of iterations to run the algorithm
     for.  If negative, no maximum will be usedMathIllegalArgumentException - if the data points are null or the number
     of clusters is larger than the number of data pointsConvergenceException - if an empty cluster is encountered and the
 emptyStrategy is set to ERRORCopyright © 2003–2016 The Apache Software Foundation. All rights reserved.