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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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
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- @Since( "1.3.0" )
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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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- @Since( "1.3.0" )
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def
setMinSupport(minSupport: Double): FPGrowth.this.type
Sets the minimal support level (default:
0.3
).Sets the minimal support level (default:
0.3
).- Annotations
- @Since( "1.3.0" )
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def
setNumPartitions(numPartitions: Int): FPGrowth.this.type
Sets 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).
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