Package org.hipparchus.clustering
Class KMeansPlusPlusClusterer<T extends Clusterable>
java.lang.Object
org.hipparchus.clustering.Clusterer<T>
org.hipparchus.clustering.KMeansPlusPlusClusterer<T>
- Type Parameters:
T
- type of the points to cluster
Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.
- See Also:
-
Nested Class Summary
Modifier and TypeClassDescriptionstatic enum
Strategies to use for replacing an empty cluster. -
Constructor Summary
ConstructorDescriptionKMeansPlusPlusClusterer
(int k) Build a clusterer.KMeansPlusPlusClusterer
(int k, int maxIterations) Build a clusterer.KMeansPlusPlusClusterer
(int k, int maxIterations, DistanceMeasure measure) Build a clusterer.KMeansPlusPlusClusterer
(int k, int maxIterations, DistanceMeasure measure, RandomGenerator random) Build a clusterer.KMeansPlusPlusClusterer
(int k, int maxIterations, DistanceMeasure measure, RandomGenerator random, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy) Build a clusterer. -
Method Summary
Modifier and TypeMethodDescriptioncluster
(Collection<T> points) Runs the K-means++ clustering algorithm.Returns theKMeansPlusPlusClusterer.EmptyClusterStrategy
used by this instance.int
getK()
Return the number of clusters this instance will use.int
Returns the maximum number of iterations this instance will use.Returns the random generator this instance will use.Methods inherited from class org.hipparchus.clustering.Clusterer
distance, getDistanceMeasure
-
Constructor Details
-
KMeansPlusPlusClusterer
public KMeansPlusPlusClusterer(int k) Build a clusterer.The default strategy for handling empty clusters that may appear during algorithm iterations is to split the cluster with largest distance variance.
The euclidean distance will be used as default distance measure.
- Parameters:
k
- the number of clusters to split the data into
-
KMeansPlusPlusClusterer
public KMeansPlusPlusClusterer(int k, int maxIterations) Build a clusterer.The default strategy for handling empty clusters that may appear during algorithm iterations is to split the cluster with largest distance variance.
The euclidean distance will be used as default distance measure.
- Parameters:
k
- 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 used.
-
KMeansPlusPlusClusterer
Build a clusterer.The default strategy for handling empty clusters that may appear during algorithm iterations is to split the cluster with largest distance variance.
- Parameters:
k
- 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 used.measure
- the distance measure to use
-
KMeansPlusPlusClusterer
public KMeansPlusPlusClusterer(int k, int maxIterations, DistanceMeasure measure, RandomGenerator random) Build a clusterer.The default strategy for handling empty clusters that may appear during algorithm iterations is to split the cluster with largest distance variance.
- Parameters:
k
- 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 used.measure
- the distance measure to userandom
- random generator to use for choosing initial centers
-
KMeansPlusPlusClusterer
public KMeansPlusPlusClusterer(int k, int maxIterations, DistanceMeasure measure, RandomGenerator random, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy) Build a clusterer.- Parameters:
k
- 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 used.measure
- the distance measure to userandom
- random generator to use for choosing initial centersemptyStrategy
- strategy to use for handling empty clusters that may appear during algorithm iterations
-
-
Method Details
-
getK
public int getK()Return the number of clusters this instance will use.- Returns:
- the number of clusters
-
getMaxIterations
public int getMaxIterations()Returns the maximum number of iterations this instance will use.- Returns:
- the maximum number of iterations, or -1 if no maximum is set
-
getRandomGenerator
Returns the random generator this instance will use.- Returns:
- the random generator
-
getEmptyClusterStrategy
Returns theKMeansPlusPlusClusterer.EmptyClusterStrategy
used by this instance.- Returns:
- the
KMeansPlusPlusClusterer.EmptyClusterStrategy
-
cluster
public List<CentroidCluster<T>> cluster(Collection<T> points) throws MathIllegalArgumentException, MathIllegalStateException Runs the K-means++ clustering algorithm.- Specified by:
cluster
in classClusterer<T extends Clusterable>
- Parameters:
points
- the points to cluster- Returns:
- a list of clusters containing the points
- Throws:
MathIllegalArgumentException
- if the data points are null or the number of clusters is larger than the number of data pointsMathIllegalStateException
- if an empty cluster is encountered and theemptyStrategy
is set toERROR
-