org.apache.spark.mllib.clustering

Class KMeansModel

Object

org.apache.spark.mllib.clustering.KMeansModel

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

StreamingKMeansModel

public class KMeansModel
extends Object
implements scala.Serializable

A clustering model for K-means. Each point belongs to the cluster with the closest center.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
KMeansModel(Vector[] clusterCenters)

Method Summary

Methods

Modifier and Type	Method and Description
Vector[]	clusterCenters()
double	computeCost(RDD<Vector> data) Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
int	k() Total number of clusters.
JavaRDD<Integer>	predict(JavaRDD<Vector> points) Maps given points to their cluster indices.
RDD<Object>	predict(RDD<Vector> points) Maps given points to their cluster indices.
int	predict(Vector point) Returns the cluster index that a given point belongs to.

Methods inherited from class Object

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

Constructor Detail

KMeansModel

public KMeansModel(Vector[] clusterCenters)

Method Detail

clusterCenters

public Vector[] clusterCenters()

k

public int k()

Total number of clusters.

predict

public int predict(Vector point)

Returns the cluster index that a given point belongs to.

predict

public RDD<Object> predict(RDD<Vector> points)

Maps given points to their cluster indices.

predict

public JavaRDD<Integer> predict(JavaRDD<Vector> points)

Maps given points to their cluster indices.

computeCost

public double computeCost(RDD<Vector> data)

Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.