org.apache.spark.mllib.clustering
Class PowerIterationClustering

Object
  org.apache.spark.mllib.clustering.PowerIterationClustering

All Implemented Interfaces:

java.io.Serializable

public class PowerIterationClustering
extends Object
implements scala.Serializable

See Also:

Serialized Form

Nested Class Summary

static class	PowerIterationClustering.Assignment :: Experimental :: Cluster assignment.
static class	PowerIterationClustering.Assignment$

Constructor Summary

PowerIterationClustering()

Method Summary

PowerIterationClusteringModel	run(JavaRDD<scala.Tuple3<Long,Long,Double>> similarities) A Java-friendly version of PowerIterationClustering.run.
PowerIterationClusteringModel	run(RDD<scala.Tuple3<Object,Object,Object>> similarities) Run the PIC algorithm.
PowerIterationClustering	setInitializationMode(String mode) Set the initialization mode.
PowerIterationClustering	setK(int k)
PowerIterationClustering	setMaxIterations(int maxIterations)

Methods inherited from class Object

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

Constructor Detail

PowerIterationClustering

public PowerIterationClustering()

Method Detail

setK

public PowerIterationClustering setK(int k)

setMaxIterations

public PowerIterationClustering setMaxIterations(int maxIterations)

setInitializationMode

public PowerIterationClustering setInitializationMode(String mode)

Set the initialization mode. This can be either "random" to use a random vector as vertex properties, or "degree" to use normalized sum similarities. Default: random.

Parameters:

mode - (undocumented)

Returns:

(undocumented)

run

public PowerIterationClusteringModel run(RDD<scala.Tuple3<Object,Object,Object>> similarities)

Run the PIC algorithm.

Parameters:

similarities - an RDD of (i, j, s,,ij,,) tuples representing the affinity matrix, which is the matrix A in the PIC paper. The similarity s,,ij,, must be nonnegative. This is a symmetric matrix and hence s,,ij,, = s,,ji,,. For any (i, j) with nonzero similarity, there should be either (i, j, s,,ij,,) or (j, i, s,,ji,,) in the input. Tuples with i = j are ignored, because we assume s,,ij,, = 0.0.

Returns:

a PowerIterationClusteringModel that contains the clustering result

run

public PowerIterationClusteringModel run(JavaRDD<scala.Tuple3<Long,Long,Double>> similarities)

A Java-friendly version of PowerIterationClustering.run.

Parameters:

similarities - (undocumented)

Returns:

(undocumented)