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class PowerIterationClustering extends 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.

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@Since( "1.3.0" )
See also

Spectral clustering (Wikipedia)

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Instance Constructors

  1. new PowerIterationClustering()

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

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

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    @Since( "1.3.0" )

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  15. def run(similarities: JavaRDD[(Long, Long, Double)]): PowerIterationClusteringModel

    A Java-friendly version of PowerIterationClustering.run.

    A Java-friendly version of PowerIterationClustering.run.

    Annotations
    @Since( "1.3.0" )
  16. def run(similarities: RDD[(Long, Long, Double)]): PowerIterationClusteringModel

    Run the PIC algorithm.

    Run the PIC algorithm.

    similarities

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

    returns

    a PowerIterationClusteringModel that contains the clustering result

    Annotations
    @Since( "1.3.0" )
  17. def run(graph: Graph[Double, Double]): PowerIterationClusteringModel

    Run the PIC algorithm on Graph.

    Run the PIC algorithm on Graph.

    graph

    an affinity matrix represented as graph, which is the matrix A in the PIC paper. The similarity sij represented as the edge between vertices (i, j) must be nonnegative. This is a symmetric matrix and hence sij = sji. For any (i, j) with nonzero similarity, there should be either (i, j, sij) or (j, i, sji) in the input. Tuples with i = j are ignored, because we assume sij = 0.0.

    returns

    a PowerIterationClusteringModel that contains the clustering result

    Annotations
    @Since( "1.5.0" )
  18. def setInitializationMode(mode: String): PowerIterationClustering.this.type

    Set the initialization 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.

    Annotations
    @Since( "1.3.0" )
  19. def setK(k: Int): PowerIterationClustering.this.type

    Set the number of clusters.

    Set the number of clusters.

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    @Since( "1.3.0" )
  20. def setMaxIterations(maxIterations: Int): PowerIterationClustering.this.type

    Set maximum number of iterations of the power iteration loop

    Set maximum number of iterations of the power iteration loop

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    @Since( "1.3.0" )
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