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:

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 ClustererBase.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

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields inherited from class jsat.clustering.kmeans.KMeans

DEFAULT_SEED_SELECTION, dm, MaxIterLimit, means, nearestCentroidDist, rand, saveCentroidDistance, seedSelection, storeMeans

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, ExecutorService threadpool, 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, ExecutorService threadpool, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, int[] designations)
protected double	cluster(DataSet dataSet, List<Double> accelCache, int k, List<Vec> means, int[] assignment, boolean exactTotal, ExecutorService threadpool, boolean returnError, Vec dataPointWeights) This is a helper method where the actual cluster is performed.
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, getListOfLists, getMeans, getParameter, getParameters, getSeedSelection, setIterationLimit, setSeedSelection, setStoreMeans, supportsWeightedData

Methods inherited from class jsat.clustering.KClustererBase

cluster, cluster, cluster, cluster

Methods inherited from class jsat.clustering.ClustererBase

cluster, cluster, createClusterListFromAssignmentArray, getDatapointsFromCluster

Methods inherited from class java.lang.Object

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

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,
                     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

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,
                     ExecutorService threadpool,
                     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

threadpool - a source of threads to run tasks

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,
                     int lowK,
                     int highK,
                     ExecutorService threadpool,
                     int[] designations)

Specified by:

cluster in interface KClusterer

Overrides:

cluster in class KMeans

cluster

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

Specified by:

cluster in interface KClusterer

Overrides:

cluster in class KMeans

cluster

protected double cluster(DataSet dataSet,
                         List<Double> accelCache,
                         int k,
                         List<Vec> means,
                         int[] assignment,
                         boolean exactTotal,
                         ExecutorService threadpool,
                         boolean returnError,
                         Vec dataPointWeights)

Description copied from class: KMeans

This is a helper method where the actual cluster is performed. This is because there are multiple strategies for modifying kmeans, but all of them require this step.
The distance metric used is trained if needed

Specified by:

cluster in class KMeans

Parameters:

dataSet - The set of data points to perform clustering on

accelCache - acceleration cache to use, or null. If null, the kmeans code will attempt to create one

k - the number of clusters

means - the initial points to use as the means. Its length is the number of means that will be searched for. These means will be altered, and should contain deep copies of the points they were drawn from. May be empty, in which case the list will be filled with some selected means

assignment - an empty temp space to store the clustering classifications. Should be the same length as the number of data points

exactTotal - determines how the objective function (return value) will be computed. If true, extra work will be done to compute the exact distance from each data point to its cluster. If false, an upper bound approximation will be used. This also impacts the value stored in KMeans.nearestCentroidDist

threadpool - the source of threads for parallel computation. If null, single threaded execution will occur

returnError - true is the sum of squared distances should be returned. false means any value can be returned. KMeans.saveCentroidDistance only applies if this is true

dataPointWeights - the weight value to use for each data point. If null, assume each point has equal weight.

Returns:

the double

clone

public KMeansPDN clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in class KMeans