FPGrowth¶
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class
pyspark.mllib.fpm.FPGrowth¶ A Parallel FP-growth algorithm to mine frequent itemsets.
Methods
train(data[, minSupport, numPartitions])Computes an FP-Growth model that contains frequent itemsets.
Methods Documentation
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classmethod
train(data: pyspark.rdd.RDD[List[T]], minSupport: float = 0.3, numPartitions: int = - 1) → pyspark.mllib.fpm.FPGrowthModel¶ Computes an FP-Growth model that contains frequent itemsets.
- Parameters
- data
pyspark.RDD The input data set, each element contains a transaction.
- minSupportfloat, optional
The minimal support level. (default: 0.3)
- numPartitionsint, optional
The number of partitions used by parallel FP-growth. A value of -1 will use the same number as input data. (default: -1)
- data
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classmethod