jsat.clustering

Class PAM

java.lang.Object

jsat.clustering.PAM

All Implemented Interfaces:

java.io.Serializable, Clusterer, KClusterer

Direct Known Subclasses:

CLARA, MEDDIT, TRIKMEDS

public class PAM
extends java.lang.Object
implements KClusterer

See Also:

Serialized Form

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
PAM	clone()
int[]	cluster(DataSet dataSet, boolean parallel, 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()
int	getMaxIterations()
int[]	getMedoids() Returns the raw array of indices that indicate which data point acted as the center for each cluster.
SeedSelectionMethods.SeedSelection	getSeedSelection()
static int	medoid(boolean parallel, java.util.Collection<java.lang.Integer> indecies, java.util.List<? extends Vec> X, DistanceMetric dm, java.util.List<java.lang.Double> accel) Computes the medoid of a sub-set of data
static int	medoid(boolean parallel, java.util.List<? extends Vec> X, DistanceMetric dm) Computes the medoid of the data
void	setDistanceMetric(DistanceMetric dm) Sets the distance metric used by this clustering algorithm
void	setMaxIterations(int iterLimit)
void	setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection) Sets the method of seed selection used by this algorithm
void	setStoreMedoids(boolean storeMedoids) If set to true the computed medoids will be stored after clustering is completed, and can then be retrieved using getMedoids().

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.clustering.KClusterer

cluster, cluster, cluster, cluster, cluster, cluster

Methods inherited from interface jsat.clustering.Clusterer

cluster, cluster, cluster, supportsWeightedData

Constructor Detail

PAM

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

PAM

public PAM(DistanceMetric dm,
           java.util.Random rand)

PAM

public PAM(DistanceMetric dm)

PAM

public PAM()

PAM

public PAM(PAM toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

setMaxIterations

public void setMaxIterations(int iterLimit)

Parameters:

iterLimit - the maximum number of iterations of the algorithm to perform

getMaxIterations

public int getMaxIterations()

Returns:

the maximum number of iterations of the algorithm to perform

setDistanceMetric

public void setDistanceMetric(DistanceMetric dm)

Sets the distance metric used by this clustering algorithm

Parameters:

dm - the distance metric to use

getDistanceMetric

public DistanceMetric getDistanceMetric()

Returns:

the distance metric to be used by this algorithm

setStoreMedoids

public void setStoreMedoids(boolean storeMedoids)

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

Parameters:

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

getMedoids

public int[] getMedoids()

Returns the raw array of indices that indicate which data point acted as the center for each cluster.

Returns:

the array of medeoid indices

setSeedSelection

public void setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection)

Sets the method of seed selection used by this algorithm

Parameters:

seedSelection - the method of seed selection to used

getSeedSelection

public SeedSelectionMethods.SeedSelection getSeedSelection()

Returns:

the method of seed selection used by this algorithm

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

clone

public PAM clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Overrides:

clone in class java.lang.Object

cluster

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

Specified by:

cluster in interface KClusterer

medoid

public static int medoid(boolean parallel,
                         java.util.List<? extends Vec> X,
                         DistanceMetric dm)

Computes the medoid of the data

Parameters:

parallel - whether or not the computation should be done using multiple cores

X - the list of all data

dm - the distance metric to get the medoid with respect to

Returns:

the index of the point in X that is the medoid

medoid

public static int medoid(boolean parallel,
                         java.util.Collection<java.lang.Integer> indecies,
                         java.util.List<? extends Vec> X,
                         DistanceMetric dm,
                         java.util.List<java.lang.Double> accel)

Computes the medoid of a sub-set of data

Parameters:

parallel - whether or not the computation should be done using multiple cores

indecies - the indexes of the points to get the medoid of

X - the list of all data

dm - the distance metric to get the medoid with respect to

accel - the acceleration cache for the distance metric

Returns:

the index value contained within indecies that is the medoid