org.apache.spark.mllib.clustering
PowerIterationClustering
Companion object PowerIterationClustering
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" )
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- PowerIterationClustering.scala
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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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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" )
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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.
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a PowerIterationClusteringModel that contains the clustering result
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- @Since( "1.3.0" )
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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.
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a PowerIterationClusteringModel that contains the clustering result
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- @Since( "1.5.0" )
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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.
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def
setK(k: Int): PowerIterationClustering.this.type
Set the number of clusters.
Set the number of clusters.
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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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