org.apache.spark.mllib.clustering

Class KMeans

Object

org.apache.spark.mllib.clustering.KMeans

All Implemented Interfaces:

java.io.Serializable, Logging

public class KMeans
extends Object
implements scala.Serializable, Logging

K-means clustering with support for multiple parallel runs and a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested, they are executed together with joint passes over the data for efficiency.

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}.

Method Summary

Methods

Modifier and Type	Method and Description
static double	fastSquaredDistance(VectorWithNorm v1, VectorWithNorm v2) Returns the squared Euclidean distance between two vectors computed by MLUtils.fastSquaredDistance(org.apache.spark.mllib.linalg.Vector, double, org.apache.spark.mllib.linalg.Vector, double, double).
static scala.Tuple2<Object,Object>	findClosest(scala.collection.TraversableOnce<VectorWithNorm> centers, VectorWithNorm point) Returns the index of the closest center to the given point, as well as the squared distance.
static String	K_MEANS_PARALLEL()
static double	pointCost(scala.collection.TraversableOnce<VectorWithNorm> centers, VectorWithNorm point) Returns the K-means cost of a given point against the given cluster centers.
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	setK(int k) Set the number of clusters to create (k).
KMeans	setMaxIterations(int maxIterations) Set maximum number of iterations to run.
KMeans	setRuns(int runs) :: Experimental :: Set the number of runs of the algorithm to execute in parallel.
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.

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

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}.

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)

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

Parameters:

data - training points stored as RDD[Array[Double}

k - number of clusters

maxIterations - max number of iterations

runs - number of parallel runs, defaults to 1. The best model is returned.

initializationMode - initialization model, either "random" or "k-means||" (default).

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.

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.

findClosest

public static scala.Tuple2<Object,Object> findClosest(scala.collection.TraversableOnce<VectorWithNorm> centers,
                                      VectorWithNorm point)

Returns the index of the closest center to the given point, as well as the squared distance.

pointCost

public static double pointCost(scala.collection.TraversableOnce<VectorWithNorm> centers,
               VectorWithNorm point)

Returns the K-means cost of a given point against the given cluster centers.

fastSquaredDistance

public static double fastSquaredDistance(VectorWithNorm v1,
                         VectorWithNorm v2)

Returns the squared Euclidean distance between two vectors computed by MLUtils.fastSquaredDistance(org.apache.spark.mllib.linalg.Vector, double, org.apache.spark.mllib.linalg.Vector, double, double).

setK

public KMeans setK(int k)

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

setMaxIterations

public KMeans setMaxIterations(int maxIterations)

Set maximum number of iterations to run. Default: 20.

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||.

setRuns

public KMeans setRuns(int runs)

:: Experimental :: Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm this many times with random starting conditions (configured by the initialization mode), then return the best clustering found over any run. Default: 1.

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.

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.

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.