jsat.clustering.kmeans

Class KMeansPDN

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.KClustererBase

jsat.clustering.kmeans.KMeans

jsat.clustering.kmeans.KMeansPDN

All Implemented Interfaces:

java.io.Serializable, Clusterer, KClusterer, Parameterized

public class KMeansPDN
extends KMeans

This class provides a method of performing KMeans clustering when the value of K is not known. It works by incrementing the value of k up to some specified maximum, and running a full KMeans for each value.

Note, by default this implementation uses a heuristic for the max value of K that is capped at 100 when using the Clusterer.cluster(jsat.DataSet) type methods.

When the value of K is specified, the implementation will simply call the regular KMeans object it was constructed with. See: Pham, D. T., Dimov, S. S.,&Nguyen, C. D. (2005). Selection of K in K-means clustering. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 219(1), 103–119. doi:10.1243/095440605X8298

See Also:

Serialized Form

Field Summary

Fields inherited from class jsat.clustering.kmeans.KMeans

DEFAULT_SEED_SELECTION

Constructor Summary

Constructors

Constructor and Description
KMeansPDN() Creates a new clusterer.
KMeansPDN(KMeans kmeans) Creates a new clustered that uses the specified object to perform clustering for all k.
KMeansPDN(KMeansPDN toCopy) Copy constructor

Method Summary

Modifier and Type	Method and Description
KMeansPDN	clone()
int[]	cluster(DataSet dataSet, boolean parallel, int[] designations) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, int lowK, int highK, boolean parallel, int[] designations)
double[]	getfKs() Returns the array of f(K) values generated for the last data set.

Methods inherited from class jsat.clustering.kmeans.KMeans

cluster, cluster, getDistanceMetric, getIterationLimit, getMeans, getSeedSelection, setIterationLimit, setSeedSelection, setStoreMeans, supportsWeightedData

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

KMeansPDN

public KMeansPDN()

Creates a new clusterer.

KMeansPDN

public KMeansPDN(KMeans kmeans)

Creates a new clustered that uses the specified object to perform clustering for all k.

Parameters:

kmeans - the k-means object to use for clustering

KMeansPDN

public KMeansPDN(KMeansPDN toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

getfKs

public double[] getfKs()

Returns the array of f(K) values generated for the last data set. The value at index i is the score for cluster i+1. Smaller values indicate better clusterings.

Returns:

the array of f(K) values, or null if no data set has been clustered

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

Overrides:

cluster in class KMeans

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

Overrides:

cluster in class KMeans

clone

public KMeansPDN clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in class KMeans