org.apache.spark.mllib.clustering

Class KMeans

Object

org.apache.spark.mllib.clustering.KMeans

All Implemented Interfaces:

java.io.Serializable

public class KMeans
extends Object
implements scala.Serializable

K-means clustering with a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al).

This is an iterative algorithm that will make multiple passes over the data, so any RDDs given to it should be cached by the user.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
KMeans() Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, runs: 1, initializationMode: "k-means||", initializationSteps: 5, epsilon: 1e-4, seed: random}.

Method Summary

Methods

Modifier and Type	Method and Description
double	getEpsilon() The distance threshold within which we've consider centers to have converged.
String	getInitializationMode() The initialization algorithm.
int	getInitializationSteps() Number of steps for the k-means|| initialization mode
int	getK() Number of clusters to create (k).
int	getMaxIterations() Maximum number of iterations allowed.
int	getRuns() This function has no effect since Spark 2.0.0.
long	getSeed() The random seed for cluster initialization.
static String	K_MEANS_PARALLEL()
static String	RANDOM()
KMeansModel	run(RDD<Vector> data) Train a K-means model on the given set of points; data should be cached for high performance, because this is an iterative algorithm.
KMeans	setEpsilon(double epsilon) Set the distance threshold within which we've consider centers to have converged.
KMeans	setInitializationMode(String initializationMode) Set the initialization algorithm.
KMeans	setInitializationSteps(int initializationSteps) Set the number of steps for the k-means|| initialization mode.
KMeans	setInitialModel(KMeansModel model) Set the initial starting point, bypassing the random initialization or k-means|| The condition model.k == this.k must be met, failure results in an IllegalArgumentException.
KMeans	setK(int k) Set the number of clusters to create (k).
KMeans	setMaxIterations(int maxIterations) Set maximum number of iterations allowed.
KMeans	setRuns(int runs) This function has no effect since Spark 2.0.0.
KMeans	setSeed(long seed) Set the random seed for cluster initialization.
static KMeansModel	train(RDD<Vector> data, int k, int maxIterations) Trains a k-means model using specified parameters and the default values for unspecified.
static KMeansModel	train(RDD<Vector> data, int k, int maxIterations, int runs) Trains a k-means model using specified parameters and the default values for unspecified.
static KMeansModel	train(RDD<Vector> data, int k, int maxIterations, int runs, String initializationMode) Trains a k-means model using the given set of parameters.
static KMeansModel	train(RDD<Vector> data, int k, int maxIterations, int runs, String initializationMode, long seed) Trains a k-means model using the given set of parameters.

Methods inherited from class Object

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

Constructor Detail

KMeans

public KMeans()

Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, runs: 1, initializationMode: "k-means||", initializationSteps: 5, epsilon: 1e-4, seed: random}.

Method Detail

RANDOM

public static String RANDOM()

K_MEANS_PARALLEL

public static String K_MEANS_PARALLEL()

train

public static KMeansModel train(RDD<Vector> data,
                int k,
                int maxIterations,
                int runs,
                String initializationMode,
                long seed)

Trains a k-means model using the given set of parameters.

Parameters:

data - Training points as an RDD of Vector types.

k - Number of clusters to create.

maxIterations - Maximum number of iterations allowed.

runs - This param has no effect since Spark 2.0.0.

initializationMode - The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

seed - Random seed for cluster initialization. Default is to generate seed based on system time.

Returns:

(undocumented)

train

public static KMeansModel train(RDD<Vector> data,
                int k,
                int maxIterations,
                int runs,
                String initializationMode)

Trains a k-means model using the given set of parameters.

Parameters:

data - Training points as an RDD of Vector types.

k - Number of clusters to create.

maxIterations - Maximum number of iterations allowed.

runs - This param has no effect since Spark 2.0.0.

initializationMode - The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

Returns:

(undocumented)

train

public static KMeansModel train(RDD<Vector> data,
                int k,
                int maxIterations)

Trains a k-means model using specified parameters and the default values for unspecified.

Parameters:

data - (undocumented)

k - (undocumented)

maxIterations - (undocumented)

Returns:

(undocumented)

train

public static KMeansModel train(RDD<Vector> data,
                int k,
                int maxIterations,
                int runs)

Trains a k-means model using specified parameters and the default values for unspecified.

Parameters:

data - (undocumented)

k - (undocumented)

maxIterations - (undocumented)

runs - (undocumented)

Returns:

(undocumented)

getK

public int getK()

Number of clusters to create (k).

Returns:

(undocumented)

setK

public KMeans setK(int k)

Set the number of clusters to create (k). Default: 2.

Parameters:

k - (undocumented)

Returns:

(undocumented)

getMaxIterations

public int getMaxIterations()

Maximum number of iterations allowed.

Returns:

(undocumented)

setMaxIterations

public KMeans setMaxIterations(int maxIterations)

Set maximum number of iterations allowed. Default: 20.

Parameters:

maxIterations - (undocumented)

Returns:

(undocumented)

getInitializationMode

public String getInitializationMode()

The initialization algorithm. This can be either "random" or "k-means||".

Returns:

(undocumented)

setInitializationMode

public KMeans setInitializationMode(String initializationMode)

Set the initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.

Parameters:

initializationMode - (undocumented)

Returns:

(undocumented)

getRuns

public int getRuns()

This function has no effect since Spark 2.0.0.

Returns:

(undocumented)

setRuns

public KMeans setRuns(int runs)

This function has no effect since Spark 2.0.0.

Parameters:

runs - (undocumented)

Returns:

(undocumented)

getInitializationSteps

public int getInitializationSteps()

Number of steps for the k-means|| initialization mode

Returns:

(undocumented)

setInitializationSteps

public KMeans setInitializationSteps(int initializationSteps)

Set the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 5 is almost always enough. Default: 5.

Parameters:

initializationSteps - (undocumented)

Returns:

(undocumented)

getEpsilon

public double getEpsilon()

The distance threshold within which we've consider centers to have converged.

Returns:

(undocumented)

setEpsilon

public KMeans setEpsilon(double epsilon)

Set the distance threshold within which we've consider centers to have converged. If all centers move less than this Euclidean distance, we stop iterating one run.

Parameters:

epsilon - (undocumented)

Returns:

(undocumented)

getSeed

public long getSeed()

The random seed for cluster initialization.

Returns:

(undocumented)

setSeed

public KMeans setSeed(long seed)

Set the random seed for cluster initialization.

Parameters:

seed - (undocumented)

Returns:

(undocumented)

setInitialModel

public KMeans setInitialModel(KMeansModel model)

Set the initial starting point, bypassing the random initialization or k-means|| The condition model.k == this.k must be met, failure results in an IllegalArgumentException.

Parameters:

model - (undocumented)

Returns:

(undocumented)

run

public KMeansModel run(RDD<Vector> data)

Train a K-means model on the given set of points; data should be cached for high performance, because this is an iterative algorithm.

Parameters:

data - (undocumented)

Returns:

(undocumented)