jsat.clustering.kmeans

Class KernelKMeans

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.KClustererBase

jsat.clustering.kmeans.KernelKMeans

All Implemented Interfaces:

java.io.Serializable, Clusterer, KClusterer, Parameterized

Direct Known Subclasses:

ElkanKernelKMeans, LloydKernelKMeans

public abstract class KernelKMeans
extends KClustererBase
implements Parameterized

Base class for various Kernel K Means implementations. Because the Kernelized version is more computationally expensive, only the clustering methods where the number of clusters is specified apriori are supported.

KernelKMeans keeps a reference to the data passed in for clustering so that queries can be conveniently answered, such as getting the closest cluster or finding the distance between means

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
KernelKMeans(KernelKMeans toCopy) Copy constructor
KernelKMeans(KernelTrick kernel)

Method Summary

Modifier and Type	Method and Description
abstract KernelKMeans	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 lowK, int highK, boolean parallel, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, int[] designations)
double	distance(Vec x, int k) Returns the distance between the given data point and the the specified cluster
double	distance(Vec x, java.util.List<java.lang.Double> qi, int k) Returns the distance between the given data point and the the specified cluster
int	findClosestCluster(Vec x) Finds the cluster ID that is closest to the given data point
int	findClosestCluster(Vec x, java.util.List<java.lang.Double> qi) Finds the cluster ID that is closest to the given data point
int	getMaximumIterations() Returns the maximum number of iterations of the KMeans algorithm that will be performed.
double	meanToMeanDistance(int k0, int k1) Computes the distance between two of the means in the clustering
void	setMaximumIterations(int iterLimit) Sets the maximum number of iterations allowed
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

Constructor Detail

KernelKMeans

public KernelKMeans(KernelTrick kernel)

Parameters:

kernel - the kernel to use

KernelKMeans

public KernelKMeans(KernelKMeans toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

setMaximumIterations

public void setMaximumIterations(int iterLimit)

Sets the maximum number of iterations allowed

Parameters:

iterLimit - the maximum number of iterations of the KMeans algorithm

getMaximumIterations

public int getMaximumIterations()

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

Returns:

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

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 lowK,
                     int highK,
                     boolean parallel,
                     int[] designations)

Specified by:

cluster in interface KClusterer

cluster

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

Specified by:

cluster in interface KClusterer

distance

public double distance(Vec x,
                       int k)

Returns the distance between the given data point and the the specified cluster

Parameters:

x - the data point to get the distance for

k - the cluster id to get the distance to

Returns:

the distance between the given data point and the specified cluster

distance

public double distance(Vec x,
                       java.util.List<java.lang.Double> qi,
                       int k)

Returns the distance between the given data point and the the specified cluster

Parameters:

x - the data point to get the distance for

qi - the query information for the given data point generated for the kernel in use. See KernelTrick.getQueryInfo(jsat.linear.Vec)

k - the cluster id to get the distance to

Returns:

the distance between the given data point and the specified cluster

findClosestCluster

public int findClosestCluster(Vec x)

Finds the cluster ID that is closest to the given data point

Parameters:

x - the data point to get the closest cluster for

Returns:

the index of the closest cluster

findClosestCluster

public int findClosestCluster(Vec x,
                              java.util.List<java.lang.Double> qi)

Finds the cluster ID that is closest to the given data point

Parameters:

x - the data point to get the closest cluster for

qi - the query information for the given data point generated for the kernel in use. See KernelTrick.getQueryInfo(jsat.linear.Vec)

Returns:

the index of the closest cluster

meanToMeanDistance

public double meanToMeanDistance(int k0,
                                 int k1)

Computes the distance between two of the means in the clustering

Parameters:

k0 - the index of the first mean

k1 - the index of the second mean

Returns:

the distance between the two

clone

public abstract KernelKMeans 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