jsat.clustering.kmeans

Class KMeans

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.KClustererBase

jsat.clustering.kmeans.KMeans

All Implemented Interfaces:

java.io.Serializable, Clusterer, KClusterer, Parameterized

Direct Known Subclasses:

ElkanKMeans, GMeans, HamerlyKMeans, KMeansPDN, NaiveKMeans, XMeans

public abstract class KMeans
extends KClustererBase
implements Parameterized

Base class for the numerous implementations of k-means that exist. This base class provides an slow heuristic approach to the selection of k.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static SeedSelectionMethods.SeedSelection	DEFAULT_SEED_SELECTION This is the default seed selection method used in ElkanKMeans.

Constructor Summary

Constructors

Constructor and Description
KMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection, java.util.Random rand)
KMeans(KMeans toCopy) Copy constructor

Method Summary

Modifier and Type	Method and Description
abstract KMeans	clone()
int[]	cluster(DataSet dataSet, boolean parallel, int[] designations) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, int[] designations) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, int clusters, boolean parallel, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, boolean parallel, int[] designations)
DistanceMetric	getDistanceMetric() Returns the distance metric in use
int	getIterationLimit() Returns the maximum number of iterations of the ElkanKMeans algorithm that will be performed.
java.util.List<Vec>	getMeans() Returns the raw list of means that were used for each class.
SeedSelectionMethods.SeedSelection	getSeedSelection()
void	setIterationLimit(int iterLimit) Sets the maximum number of iterations allowed
void	setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection) Sets the method of seed selection to use for this algorithm.
void	setStoreMeans(boolean storeMeans) If set to true the computed means will be stored after clustering is completed, and can then be retrieved using getMeans().
boolean	supportsWeightedData() Indicates whether the model knows how to cluster using weighted data points.

Methods inherited from class jsat.clustering.ClustererBase

createClusterListFromAssignmentArray, getDatapointsFromCluster

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Methods inherited from interface jsat.clustering.KClusterer

cluster, cluster, cluster, cluster, cluster, cluster

Methods inherited from interface jsat.clustering.Clusterer

cluster, cluster

Field Detail

DEFAULT_SEED_SELECTION

public static final SeedSelectionMethods.SeedSelection DEFAULT_SEED_SELECTION

This is the default seed selection method used in ElkanKMeans. When used with the EuclideanDistance, it selects seeds that are log optimal with a high probability.

Constructor Detail

KMeans

public KMeans(DistanceMetric dm,
              SeedSelectionMethods.SeedSelection seedSelection,
              java.util.Random rand)

KMeans

public KMeans(KMeans toCopy)

Copy constructor

Parameters:

toCopy -

Method Detail

setIterationLimit

public void setIterationLimit(int iterLimit)

Sets the maximum number of iterations allowed

Parameters:

iterLimit - the maximum number of iterations of the ElkanKMeans algorithm

getIterationLimit

public int getIterationLimit()

Returns the maximum number of iterations of the ElkanKMeans algorithm that will be performed.

Returns:

the maximum number of iterations of the ElkanKMeans algorithm that will be performed.

setStoreMeans

public void setStoreMeans(boolean storeMeans)

If set to true the computed means will be stored after clustering is completed, and can then be retrieved using getMeans().

Parameters:

storeMeans - true if the means should be stored for later, false to discard them once clustering is complete.

getMeans

public java.util.List<Vec> getMeans()

Returns the raw list of means that were used for each class.

Returns:

the list of means for each class

setSeedSelection

public void setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection)

Sets the method of seed selection to use for this algorithm. SeedSelectionMethods.SeedSelection.KPP is recommended for this algorithm in particular.

Parameters:

seedSelection - the method of seed selection to use

getSeedSelection

public SeedSelectionMethods.SeedSelection getSeedSelection()

Returns:

the method of seed selection used

getDistanceMetric

public DistanceMetric getDistanceMetric()

Returns the distance metric in use

Returns:

the distance metric in use

cluster

public int[] cluster(DataSet dataSet,
                     int[] designations)

Description copied from interface: Clusterer

Performs clustering on the given data set. Parameters may be estimated by the method, or other heuristics performed.

Specified by:

cluster in interface Clusterer

Parameters:

dataSet - the data set to perform clustering on

designations - the array which will contain the designated values. The array will be altered and returned by the function. If null is given, a new array will be created and returned.

Returns:

an array indicating for each value indicating the cluster designation. This is the same array as designations, or a new one if the input array was null

cluster

public int[] cluster(DataSet dataSet,
                     boolean parallel,
                     int[] designations)

Description copied from interface: Clusterer

Performs clustering on the given data set. Parameters may be estimated by the method, or other heuristics performed.

Specified by:

cluster in interface Clusterer

Parameters:

dataSet - the data set to perform clustering on

parallel - true if multiple threads should be used to perform clustering. false if it should be done in a single threaded manner.

designations - the array which will contain the designated values. The array will be altered and returned by the function. If null is given, a new array will be created and returned.

Returns:

the int[]

cluster

public int[] cluster(DataSet dataSet,
                     int clusters,
                     boolean parallel,
                     int[] designations)

Specified by:

cluster in interface KClusterer

cluster

public int[] cluster(DataSet dataSet,
                     int lowK,
                     int highK,
                     boolean parallel,
                     int[] designations)

Specified by:

cluster in interface KClusterer

clone

public abstract KMeans clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in class KClustererBase

supportsWeightedData

public boolean supportsWeightedData()

Description copied from interface: Clusterer

Indicates whether the model knows how to cluster using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Specified by:

supportsWeightedData in interface Clusterer

Returns:

true if the model supports weighted data, false otherwise