PowerIterationClustering¶
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class
pyspark.mllib.clustering.
PowerIterationClustering
[source]¶ Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by [[http://www.cs.cmu.edu/~frank/papers/icml2010-pic-final.pdf 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.
New in version 1.5.0.
Methods
Methods Documentation
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classmethod
train
(rdd, k, maxIterations=100, initMode='random')[source]¶ - Parameters
rdd – An RDD of (i, j, sij) tuples representing the affinity matrix, which is the matrix A in the PIC paper. The similarity sijmust 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 it is assumed sij= 0.0.
k – Number of clusters.
maxIterations – Maximum number of iterations of the PIC algorithm. (default: 100)
initMode – Initialization mode. This can be either “random” to use a random vector as vertex properties, or “degree” to use normalized sum similarities. (default: “random”)
New in version 1.5.0.
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classmethod