org.apache.spark.mllib.fpm

FPGrowth

class FPGrowth extends Logging with Serializable

:: Experimental ::

A parallel FP-growth algorithm to mine frequent itemsets. The algorithm is described in Li et al., PFP: Parallel FP-Growth for Query Recommendation. PFP distributes computation in such a way that each worker executes an independent group of mining tasks. The FP-Growth algorithm is described in Han et al., Mining frequent patterns without candidate generation.

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@Experimental()
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  1. new FPGrowth()

    Constructs a default instance with default parameters {minSupport: 0.3, numPartitions: same as the input data}.

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  29. final def notifyAll(): Unit

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  30. def run[Item, Basket <: Iterable[Item]](data: JavaRDD[Basket]): FPGrowthModel[Item]

  31. def run[Item](data: RDD[Array[Item]])(implicit arg0: ClassTag[Item]): FPGrowthModel[Item]

    Computes an FP-Growth model that contains frequent itemsets.

    Computes an FP-Growth model that contains frequent itemsets.

    data

    input data set, each element contains a transaction

    returns

    an FPGrowthModel

  32. def setMinSupport(minSupport: Double): FPGrowth.this.type

    Sets the minimal support level (default: 0.3).

  33. def setNumPartitions(numPartitions: Int): FPGrowth.this.type

    Sets the number of partitions used by parallel FP-growth (default: same as input data).

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