class FPGrowth extends Logging with Serializable
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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- @Since("1.3.0")
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- FPGrowth.scala
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-    new FPGrowth()Constructs a default instance with default parameters {minSupport: 0.3, numPartitions: same as the input data}.Constructs a default instance with default parameters {minSupport: 0.3, numPartitions: same as the input data}.- Annotations
- @Since("1.3.0")
 
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-    def run[Item, Basket <: Iterable[Item]](data: JavaRDD[Basket]): FPGrowthModel[Item]Java-friendly version of run.Java-friendly version of run.- Annotations
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-    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
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-    def setMinSupport(minSupport: Double): FPGrowth.this.typeSets the minimal support level (default: 0.3).Sets the minimal support level (default: 0.3).- Annotations
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-    def setNumPartitions(numPartitions: Int): FPGrowth.this.typeSets the number of partitions used by parallel FP-growth (default: same as input data). Sets the number of partitions used by parallel FP-growth (default: same as input data). - Annotations
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- (Since version 9)