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

Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by Lin and Cohen. From the abstract: PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data.

param: k Number of clusters. param: maxIterations Maximum number of iterations of the PIC algorithm. param: initMode 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.

See Also:

Spectral clustering (Wikipedia), Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	PowerIterationClustering.Assignment Cluster assignment.
static class	PowerIterationClustering.Assignment$

Constructor Summary

Constructors

Constructor and Description
PowerIterationClustering() Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100, initMode: "random"}.

Method Summary

Modifier and Type	Method and Description
static void	org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)
static org.slf4j.Logger	org$apache$spark$internal$Logging$$log_()
PowerIterationClusteringModel	run(Graph<Object,Object> graph) Run the PIC algorithm on Graph.
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) Set the number of clusters.
PowerIterationClustering	setMaxIterations(int maxIterations) Set maximum number of iterations of the power iteration loop

Methods inherited from class Object

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

Constructor Detail

PowerIterationClustering

public PowerIterationClustering()

Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100, initMode: "random"}.

Method Detail

org$apache$spark$internal$Logging$$log_

public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()

org$apache$spark$internal$Logging$$log__$eq

public static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)

setK

public PowerIterationClustering setK(int k)

Set the number of clusters.

Parameters:

k - (undocumented)

Returns:

(undocumented)

setMaxIterations

public PowerIterationClustering setMaxIterations(int maxIterations)

Set maximum number of iterations of the power iteration loop

Parameters:

maxIterations - (undocumented)

Returns:

(undocumented)

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(Graph<Object,Object> graph)

Run the PIC algorithm on Graph.

Parameters:

graph - an affinity matrix represented as graph, which is the matrix A in the PIC paper. The similarity s,,ij,, represented as the edge between vertices (i, j) 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(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)