jsat.clustering.kmeans

Class NaiveKMeans

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.KClustererBase

jsat.clustering.kmeans.KMeans

jsat.clustering.kmeans.NaiveKMeans

All Implemented Interfaces:

Serializable, Clusterer, KClusterer, Parameterized

public class NaiveKMeans
extends KMeans

An implementation of Lloyd's K-Means clustering algorithm using the naive algorithm. This implementation exists mostly for comparison as a base line and educational reasons. For efficient exact k-Means, use ElkanKMeans

This implementation is parallel, but does not support any of the clustering methods that do not specify the number of clusters.

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
NaiveKMeans() Creates a new naive k-Means cluster using k-means++ for the seed selection and the EuclideanDistance
NaiveKMeans(DistanceMetric dm) Creates a new naive k-Means cluster using k-means++ for the seed selection.
NaiveKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection) Creates a new naive k-Means cluster
NaiveKMeans(DistanceMetric dm, SeedSelectionMethods.SeedSelection seedSelection, Random rand) Creates a new naive k-Means cluster
NaiveKMeans(NaiveKMeans toCopy) Copy constructor

Method Summary

Modifier and Type	Method and Description
NaiveKMeans	clone()
protected double	cluster(DataSet dataSet, List<Double> accelCacheInit, 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.

Methods inherited from class jsat.clustering.kmeans.KMeans

cluster, cluster, cluster, cluster, 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

NaiveKMeans

public NaiveKMeans()

Creates a new naive k-Means cluster using k-means++ for the seed selection and the EuclideanDistance

NaiveKMeans

public NaiveKMeans(DistanceMetric dm)

Creates a new naive k-Means cluster using k-means++ for the seed selection.

Parameters:

dm - the distance function to use

NaiveKMeans

public NaiveKMeans(DistanceMetric dm,
                   SeedSelectionMethods.SeedSelection seedSelection)

Creates a new naive k-Means cluster

Parameters:

dm - the distance function to use

seedSelection - the method of selecting the initial seeds

NaiveKMeans

public NaiveKMeans(DistanceMetric dm,
                   SeedSelectionMethods.SeedSelection seedSelection,
                   Random rand)

Creates a new naive k-Means cluster

Parameters:

dm - the distance function to use

seedSelection - the method of selecting the initial seeds

rand - the source of randomness to use

NaiveKMeans

public NaiveKMeans(NaiveKMeans toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

cluster

protected double cluster(DataSet dataSet,
                         List<Double> accelCacheInit,
                         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

accelCacheInit - 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 NaiveKMeans clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in class KMeans