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:

java.io.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.

See Also:

Serialized Form

Field Summary

Fields inherited from class jsat.clustering.kmeans.KMeans

DEFAULT_SEED_SELECTION

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, java.util.Random rand) Creates a new naive k-Means cluster
NaiveKMeans(NaiveKMeans toCopy) Copy constructor

Method Summary

Modifier and Type	Method and Description
NaiveKMeans	clone()

Methods inherited from class jsat.clustering.kmeans.KMeans

cluster, cluster, 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

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,
                   java.util.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

clone

public NaiveKMeans clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in class KMeans