PowerIterationClustering¶
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class
pyspark.mllib.clustering.PowerIterationClustering[source]¶ Power Iteration Clustering (PIC), a scalable graph clustering algorithm.
Developed by Lin and Cohen [1]. 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.
- 1
Lin, Frank & Cohen, William. (2010). Power Iteration Clustering. http://www.cs.cmu.edu/~frank/papers/icml2010-pic-final.pdf
Methods
train(rdd, k[, maxIterations, initMode])Train PowerIterationClusteringModel
Methods Documentation
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classmethod
train(rdd: pyspark.rdd.RDD[Tuple[int, int, float]], k: int, maxIterations: int = 100, initMode: str = 'random') → pyspark.mllib.clustering.PowerIterationClusteringModel[source]¶ Train PowerIterationClusteringModel
New in version 1.5.0.
- Parameters
- rdd
pyspark.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.
- kint
Number of clusters.
- maxIterationsint, optional
Maximum number of iterations of the PIC algorithm. (default: 100)
- initModestr, optional
Initialization mode. This can be either “random” to use a random vector as vertex properties, or “degree” to use normalized sum similarities. (default: “random”)
- rdd